Systems, apparatus, and methods related to modeling, monitoring, and/or managing metabolism

ABSTRACT

Systems, apparatus, and methods related to modeling, monitoring, and/or managing metabolism of a subject include measuring a respiratory quotient (RQ) level in a subject and/or optimizing and executing a nonlinear feedback model to model energy substrate utilization in the subject based on at least one of a macronutrient composition and caloric value of food consumed by the subject, an intensity and duration of activity by the subject, a rate and maximum capacity of glycogen storage in the subject, a rate and maximum capacity of de novo lipogenesis in the subject, a quality and duration of sleep by the subject, and/or an RQ level in the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.15/221,313, filed on Jul. 27, 2016, which in turn claims the prioritybenefit, under 35 U.S.C. 119(e), of U.S. Provisional Patent Application62/197,324, filed on Jul. 27, 2015. Each of these applications isincorporated herein by reference in its entirety.

GOVERNMENT SUPPORT STATEMENT

This invention was made with Government support under Contract No.FA8721-05-C-0002 awarded by the U.S. Air Force. The Government hascertain rights in the invention.

TECHNICAL FIELD

The present disclosure relates generally to biofeedback systems,apparatus, and methods. More specifically, the present disclosurerelates to systems, apparatus, and methods related to modeling,monitoring, and/or managing a metabolic state of a subject.

BACKGROUND

Obesity in humans has steadily increased worldwide. This trend is notjust confined to adults-currently, over one-third of U.S. adults areobese-but also evident in children and adolescents. Comorbiditiesassociated with obesity include type 2 diabetes, cancer (e.g.,pancreatic and prostate cancers), cardiovascular disease, asthma,gallbladder disease, osteoarthritis, and chronic back pain. The mostprevalent comorbidity associated with obesity is type 2 diabetes, whichis estimated to cost the United States in excess of $300 billion peryear.

Diet plans for preventing or reducing obesity provide often conflictingand sometimes even diametrically opposed advice for which macronutrientspromote weight loss. More recently, the “calorie-in/calorie-out” (CICO)model of metabolism has increased in popularity. The use of the calorieto attribute energy to food sources has been in place since themid-1800s. FIG. 1 is a diagram illustrating the “calorie-in/calorie-out”(CICO) model 100, which posits that an imbalance between a subject'senergy needs (calories out 102, e.g., 1500-3000 Cal/day) and the amountof calories that the subject consumes (calories in 104, e.g., 1000-3000Cal/day) results in either weight gain (virtually unlimited caloriestorage 106) or weight loss (calorie burning 108). That is, if thesubject consumes calories in excess of the subject's energy needs, theexcess calories will not be burned or excreted, but rather stored asglycogen or as adipose tissue, thereby contributing to obesity andcomorbidities.

Many diet plans now rely on tracking food calories as a simplistic meansto manage weight. Diet plans that are structured around the CICO modelpromote overt control of calorie intake and/or an exercise regimen tocreate an energy imbalance.

While the CICO model relies on the first law of thermodynamics (i.e.,energy cannot be created or destroyed), the underlying assumption of theCICO model is that all food calories are alike. As such, caloriecounting and other diet plans that are structured around the CICO modelare not only tedious to implement and prone to error, but often ignorethe impact that different macronutrient mixes and exercise intensitieshave on an individual's homeostatic control system. As a consequence,diet plans focused solely on energy balance (the CICO model) have failedto reduce the worldwide trend of increasing obesity and comorbiditiesdespite their popularity.

SUMMARY

The inventors have recognized and appreciated that existing metabolicmodels, particularly the CICO model, fail to capture the physiologicalcomplexity of metabolism and therefore do not provide sufficientguidance to reverse obesity trends in individuals or populations.Instead, the inventors herein provide a metabolic state model based onavoiding elevated blood glucose levels through proper selection ofdietary macronutrients and exercise sufficient to enable the body'shomeostatic system to achieve and maintain a healthy body weightaccording to some embodiments.

In part, the inventors have recognized and appreciated that the kinds offoods consumed and activities performed by a subject, in concert withtheir unique genetic makeup, directly influence the subject's metabolicstate-fat/carbohydrate/protein burning, fat/carbohydrate/proteinstoring, or neutral-and thereby account greatly for weight gain andweight loss. According to some embodiments, the inventors have developedpersonal respiratory quotient (RQ) measurement devices and methods forproviding on-demand feedback indicative of a subject's real-timemetabolic state and guiding the subject's macronutrient intake and/oractivity levels to promote a desired metabolic state and/or achieve andmaintain a healthy body weight.

In one embodiment, a system for managing metabolism of a subjectincludes at least one input device for obtaining data related to thesubject, at least one memory device for storing the data related to thesubject and processor-executable instructions, and at least oneprocessor in communication with the at least one input device and the atleast one memory device. Upon execution of the processor-executableinstructions, the at least one processor determines, from the datarelated to the subject, metabolic data characterizing energy substrateutilization in the subject, the metabolic data including RQ dataacquired from the subject, and controls operation of a nonlinearfeedback model to determine, based on the metabolic data, a target valueof one or more energy substrate utilization variables, at least one ofwhich maintains and alters energy substrate utilization in the subject.The nonlinear feedback model is optimized to model energy substrateutilization in the subject based on at least one of a macronutrientcomposition and caloric value of food consumed by the subject, anintensity and duration of activity by the subject, a rate and maximumcapacity of glycogen storage in the subject, a rate and maximum capacityof de novo lipogenesis in the subject, and a quality and duration ofsleep by the subject. The one or more energy substrate utilizationvariables include at least one of the macronutrient composition andcaloric value of food consumed by the subject and the intensity andduration of activity by the subject.

In one embodiment, a system for optimizing a nonlinear feedback model ofenergy substrate utilization in a subject includes at least one inputdevice for obtaining data related to the subject, at least one memorydevice for storing the data related to the subject andprocessor-executable instructions, and at least one processor incommunication with the at least one input device and the at least onememory device. Upon execution of the processor-executable instructions,the at least one processor determines from the data related to thesubject a macronutrient composition and caloric value of food consumedby the subject, an intensity and duration of activity by the subject, arate and maximum capacity of glycogen storage in the subject, and a rateand maximum capacity of de novo lipogenesis in the subject. The at leastone processor further optimizes the nonlinear feedback model to modelenergy substrate utilization in the subject based on the macronutrientcomposition and caloric value of food consumed by the subject, theintensity and duration of activity by the subject, the rate and maximumcapacity of glycogen storage in the subject, and the rate and maximumcapacity of de novo lipogenesis in the subject.

In one embodiment, a system for managing body weight of a subjectincludes at least one input device for obtaining data related to thesubject, at least one memory device for storing the data related to thesubject and processor-executable instructions, and at least oneprocessor in communication with the at least one input device and the atleast one memory device. Upon execution of the processor-executableinstructions, the at least one processor determines from the datarelated to the subject at least one initial physiological parameterassociated with the subject, the at least one initial physiologicalparameter including an initial body weight of the subject, and controlsoperation of a nonlinear feedback model to determine, based on the atleast one initial physiological parameter, a target value of one or moreenergy substrate utilization variables that at least one of maintainsand alters the body weight of the subject. The nonlinear feedback modelis optimized to model energy substrate utilization in the subject basedon at least one of a macronutrient composition and caloric value of foodconsumed by the subject, an intensity and duration of activity by thesubject, a rate and maximum capacity of glycogen storage in the subject,a rate and maximum capacity of de novo lipogenesis in the subject, and aquality and duration of sleep by the subject. The one or more energysubstrate utilization variables comprise at least one of themacronutrient composition and caloric value of food consumed by thesubject and the intensity and duration of activity by the subject.

In one embodiment, an apparatus for measuring an RQ level in a subjectincludes a first input port for receiving respired air from the subject,a measurement chamber for receiving the respired air from the firstinput port, the measurement chamber being in fluid communication withthe first input port, a first sensor located in the measurement chamber,the first sensor for measuring a series of oxygen levels in themeasurement chamber, a second sensor located in the measurement chamber,the second sensor for measuring a series of carbon dioxide levels in themeasurement chamber at a temporal rate sufficient to ascertain arespiration rate of the subject, at least one output interface, at leastone memory for storing processor-executable instructions, the series ofoxygen level measurements, and the series of carbon dioxide levelmeasurements, and at least one processor coupled to the first sensor,the second sensor, the at least one output interface, and the at leastone memory. Upon execution of the processor-executable instructions, theat least one processor obtains a first portion of the series of carbondioxide level measurements, determines a first respiration rate based onthe first portion of the series of carbon dioxide level measurements.The at least one processor further iterates steps of obtaining asubsequent portion of the series of carbon dioxide level measurements,determining a subsequent respiration rate based on the subsequentportion of the series of carbon dioxide level measurements, andcomparing the subsequent respiration rate to at least one priorrespiration rate until a stable breathing pattern is identified. The atleast one processor then obtains a stable breathing pattern portion ofthe series of oxygen level measurements and a stable breathing patternportion of the series of carbon dioxide level measurements, determinesan average minimum oxygen level for a respiration cycle from the stablebreathing pattern portion of the series of oxygen level measurements,determines an average maximum carbon dioxide level for a respirationcycle from the stable breathing pattern portion of the series of carbondioxide level measurements, calculates the RQ level from the averageminimum oxygen level and the average maximum carbon dioxide level, andat least one of displays and transmits, via the at least one outputinterface, the calculated RQ level.

In an embodiment, the at least one processor controls an ambient aircalibration process including the steps of comparing at least onemeasurement to at least one expected value for ambient air. If the atleast one measurement is from the first sensor and sufficientlydifferent from the at least one expected value for ambient air, the atleast one processor may perform a span calibration process on the firstsensor to determine and apply a gain correction to subsequentmeasurements from the first sensor. If the at least one measurement isfrom the second sensor and sufficiently different from the at least oneexpected value for ambient air, the at least one processor may perform azero-point calibration process on the second sensor to determine andapply an offset correction to subsequent measurements from the secondsensor.

In an embodiment, the at least one expected value for ambient air at 760mm Hg is at least one of about 19.5% (v/v) to about 23.5% (v/v) oxygenand about 250 ppm to about 5,000 ppm carbon dioxide. The at least oneexpected value for ambient air at 760 mm Hg may be at least one of about20.9% (v/v) oxygen and about 400 ppm carbon dioxide. The ambient aircalibration process may be performed each time the first sensor and thesecond sensor are initiated. The ambient air calibration may also employmeasured relative humidity to adjust the percent oxygen to account forwater vapor content. The ambient air calibration process further mayinclude storing in the at least one memory at least one of the gaincorrection applied to subsequent measurements from the first sensor andthe offset correction applied to subsequent measurements from the secondsensor, such that a history of ambient air measurement drift ismaintained for reference.

In an embodiment, the apparatus further includes a second input port forreceiving a carbon dioxide cartridge for calibrating at least one of thefirst sensor and the second sensor, the second input port being in fluidcommunication with the measurement chamber such that the measurementchamber receives carbon dioxide released from the carbon dioxidecartridge.

In an embodiment, the at least one processor controls a full calibrationprocess, the full calibration process including allowing the measurementchamber to fill with ambient air, setting a span value for the firstsensor to an expected value for the ambient air, setting a zero-pointvalue for the second sensor to an expected value for the ambient air,coupling the second input port with the carbon dioxide cartridge forreleasing carbon dioxide from the carbon dioxide cartridge, releasingthe carbon dioxide into the measurement chamber, setting a zero-pointvalue for the first sensor to zero once the carbon dioxide displaces theambient air in the measurement chamber, and iteratively measuring oxygenlevels with the first sensor in the measurement chamber as the ambientair displaces the carbon dioxide in the measurement chamber until apredetermined oxygen level is measured, the predetermined oxygen levelindicating the span value for the second sensor. The predeterminedoxygen level may be about 16.7%, and the span value for the secondsensor may be about 20%.

In an embodiment, the apparatus further includes a component timed toprevent fluid communication between the first input port and themeasurement chamber during an initial portion of each respiration cycleand allow fluid communication between the first input port and themeasurement chamber during an end-tidal portion of each respirationcycle. The timed component may be at least one of a mechanical shutter,a vacuum pump, and a purge fan. The first input port may be compatiblewith at least one of a mouthpiece and a sample tube for coupling theinput port to at least one of the mouth and a nostril of the subject.The at least one of the mouthpiece and the sample tube may be at leastone of disposable and for hands-free use. The mouthpiece may be arepurposed sport bottle cap. The apparatus further may include a heatingelement for at least one of preventing and reducing condensation on thefirst sensor and the second sensor.

In one embodiment, a kit for measuring an RQ level in a subject includesa carbon dioxide cartridge, at least one of a mouthpiece and a sampletube for coupling to at least one of the mouth and a nostril of thesubject, and a device for measuring the RQ level in a subject. Thedevice includes a first input port for receiving, via the at least oneof the mouthpiece and the sample tube, respired air from the subject, ameasurement chamber for receiving the respired air from the first inputport, the measurement chamber being in fluid communication with thefirst input port, a first sensor located in the measurement chamber, thefirst sensor for measuring a series of oxygen levels in the measurementchamber, a second sensor located in the measurement chamber, the secondsensor for measuring a series of carbon dioxide levels in themeasurement chamber at a temporal rate sufficient to ascertain arespiration rate of the subject, a second input port for receiving thecarbon dioxide cartridge for calibrating at least one of the firstsensor and the second sensor, the second input port being in fluidcommunication with the measurement chamber such that the measurementchamber receives carbon dioxide released from the carbon dioxidecartridge, at least one output interface, at least one memory forstoring processor-executable instructions, the series of oxygen levelmeasurements, and the series of carbon dioxide level measurements, atleast one processor coupled to the first sensor, the second sensor, theat least one output interface, and the at least one memory. Uponexecution of the processor-executable instructions, the at least oneprocessor obtains a first portion of the series of carbon dioxide levelmeasurements, determines a first respiration rate based on the firstportion of the series of carbon dioxide level measurements, iteratessteps of obtaining a subsequent portion of the series of carbon dioxidelevel measurements, determining a subsequent respiration rate based onthe subsequent portion of the series of carbon dioxide levelmeasurements, and comparing the subsequent respiration rate to at leastone prior respiration rate until a stable breathing pattern isidentified, obtains a stable breathing pattern portion of the series ofoxygen level measurements and a stable breathing pattern portion of theseries of carbon dioxide level measurements, determines an averageminimum oxygen level for a respiration cycle from the stable breathingpattern portion of the series of oxygen level measurements, determinesan average maximum carbon dioxide level for a respiration cycle from thestable breathing pattern portion of the series of carbon dioxide levelmeasurements, calculates the RQ level from the average minimum oxygenlevel and the average maximum carbon dioxide level, and at least one ofdisplays and transmits, via the at least one output interface, thecalculated RQ level.

In one embodiment, a computer-facilitated method for measuring an RQlevel in a subject includes receiving, in a measurement chamber via aninput port in fluid communication with the measurement chamber, respiredair from the subject, obtaining, via a first sensor located in themeasurement chamber, a first portion of a series of oxygen levelmeasurements, obtaining, via a second sensor located in the measurementchamber, a first portion of a series of carbon dioxide levelmeasurements, and determining, via at least one processor, a firstrespiration rate based on the first portion of the series of carbondioxide level measurements. The method further includes iterating stepsof obtaining a subsequent portion of the series of carbon dioxide levelmeasurements, determining, via the at least one processor, a subsequentrespiration rate based on the subsequent portion of the series of carbondioxide level measurements, and comparing, via the at least oneprocessor, the subsequent respiration rate to at least one priorrespiration rate until a stable breathing pattern is identified. Themethod then includes obtaining a stable breathing pattern portion of theseries of oxygen level measurements and a stable breathing patternportion of the series of carbon dioxide level measurements, determining,via the at least one processor, an average minimum oxygen level for arespiration cycle from the stable breathing pattern portion of theseries of oxygen level measurements, determining, via the at least oneprocessor, an average maximum carbon dioxide level for a respirationcycle from the stable breathing pattern portion of the series of carbondioxide level measurements, calculating, via the at least one processor,the RQ level from the average minimum oxygen level and the averagemaximum carbon dioxide level, and at least one of displaying andtransmitting, via at least one output interface, the calculated RQlevel.

In one embodiment, a computer-facilitated method for managing metabolismof a subject includes determining from data related to the subjectobtained via at least one input device a macronutrient composition andcaloric value of food consumed by the subject, an intensity and durationof activity by the subject, a rate and maximum capacity of glycogenstorage in the subject, and a rate and maximum capacity of de novolipogenesis in the subject. The method also includes optimizing, via atleast one processor, a nonlinear feedback model to model energysubstrate utilization in the subject based on the macronutrientcomposition and caloric value of food consumed by the subject, theintensity and duration of activity by the subject, the rate and maximumcapacity of glycogen storage in the subject, and the rate and maximumcapacity of de novo lipogenesis in the subject. The method furtherincludes obtaining metabolic data for the energy substrate utilizationin the subject, the metabolic data including RQ data acquired from thesubject, and controlling, via the at least one processor, operation ofthe optimized nonlinear feedback model based on the metabolic data todetermine a target value of one or more energy substrate utilizationvariables that at least one of maintains and increases energy substrateutilization in the subject. The one or more energy substrate utilizationvariables include at least one of the macronutrient composition andcaloric value of food consumed by the subject and the intensity andduration of activity by the subject.

In one embodiment, a computer-facilitated method for managing metabolismof a subject includes determining from data related to the subjectobtained via at least one input device at least one of a macronutrientcomposition and caloric value of food consumed by the subject, anintensity and duration of activity by the subject, a rate and maximumcapacity of glycogen storage in the subject, a rate and maximum capacityof de novo lipogenesis in the subject, and a quality and duration ofsleep by the subject. The method also includes optimizing, via at leastone processor, a nonlinear feedback model to model energy substrateutilization in the subject based on at least one of the macronutrientcomposition and caloric value of food consumed by the subject, theintensity and duration of activity by the subject, the rate and maximumcapacity of glycogen storage in the subject, the rate and maximumcapacity of de novo lipogenesis in the subject, and the quality andduration of sleep by the subject. The method further includes obtainingmetabolic data for the energy substrate utilization in the subject, themetabolic data including RQ data acquired from the subject, andcontrolling, via the at least one processor, operation of the optimizednonlinear feedback model based on the metabolic data to determine atarget value of one or more energy substrate utilization variables thatat least one of maintains and increases energy substrate utilization inthe subject. The one or more energy substrate utilization variablesinclude at least one of the macronutrient composition and caloric valueof food consumed by the subject and the intensity and duration ofactivity by the subject.

In one embodiment, a computer-facilitated method for modeling metabolismin a subject includes determining from data related to the subjectobtained via at least one input device a macronutrient composition andcaloric value of food consumed by the subject, an intensity and durationof activity by the subject, a rate and maximum capacity of glycogenstorage in the subject, and a rate and maximum capacity of de novolipogenesis in the subject. The method also includes optimizing, via atleast one processor, a nonlinear feedback model to model energysubstrate utilization in the subject based on the macronutrientcomposition and caloric value of food consumed by the subject, theintensity and duration of activity by the subject, the rate and maximumcapacity of glycogen storage in the subject, and the rate and maximumcapacity of de novo lipogenesis in the subject.

In one embodiment, a computer-facilitated method for managing bodyweight of a subject includes determining from data related to thesubject obtained via at least one input device a macronutrientcomposition and caloric value of food consumed by the subject, anintensity and duration of activity by the subject, a rate and maximumcapacity of glycogen storage in the subject, and a rate and maximumcapacity of de novo lipogenesis in the subject. The method also includesoptimizing, via at least one processor, a nonlinear feedback model tomodel energy substrate utilization in the subject based on themacronutrient composition and caloric value of food consumed by thesubject, the intensity and duration of activity by the subject, the rateand maximum capacity of glycogen storage in the subject, and the rateand maximum capacity of de novo lipogenesis in the subject. The methodfurther includes obtaining at least one initial physiological parameterassociated with the subject, the at least one initial physiologicalparameter including an initial body weight of the subject, andcontrolling, via the at least one processor, operation of the optimizednonlinear feedback model based on the at least one initial physiologicalparameter to determine a target value of one or more energy substrateutilization variables that at least one of maintains and alters the bodyweight of the subject. The one or more energy substrate utilizationvariables include at least one of the macronutrient composition andcaloric value of food consumed by the subject and the intensity andduration of activity by the subject.

In one embodiment, a computer-facilitated method for managing bodyweight of a subject includes determining from data related to thesubject obtained via at least one input device at least one of amacronutrient composition and caloric value of food consumed by thesubject, an intensity and duration of activity by the subject, a rateand maximum capacity of glycogen storage in the subject, a rate andmaximum capacity of de novo lipogenesis in the subject, and a qualityand duration of sleep by the subject. The method also includesoptimizing, via at least one processor, a nonlinear feedback model tomodel energy substrate utilization in the subject based on at least oneof the macronutrient composition and caloric value of food consumed bythe subject, the intensity and duration of activity by the subject, therate and maximum capacity of glycogen storage in the subject, the rateand maximum capacity of de novo lipogenesis in the subject, and thequality and duration of sleep by the subject. The method furtherincludes obtaining at least one initial physiological parameterassociated with the subject, the at least one initial physiologicalparameter including an initial body weight of the subject, andcontrolling, via the at least one processor, operation of the optimizednonlinear feedback model based on the at least one initial physiologicalparameter to determine a target value of one or more energy substrateutilization variables that at least one of maintains and alters the bodyweight of the subject. The one or more energy substrate utilizationvariables comprise at least one of the macronutrient composition andcaloric value of food consumed by the subject and the intensity andduration of activity by the subject.

In an embodiment, the at least one initial physiological parameterfurther includes at least one of height, age, gender, body mass index(BMI), body fat percentage, waist circumference, hip circumference, andchest circumference.

In an embodiment, the nonlinear feedback model is optimized furtherbased on a quality and duration of sleep by the subject.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail below (provided suchconcepts are not mutually inconsistent) are contemplated as being partof the inventive subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of thisdisclosure are contemplated as being part of the inventive subjectmatter disclosed herein. It should also be appreciated that terminologyexplicitly employed herein that also may appear in any disclosureincorporated by reference should be accorded a meaning most consistentwith the particular concepts disclosed herein.

Other systems, processes, and features will become apparent to thoseskilled in the art upon examination of the following drawings anddetailed description. It is intended that all such additional systems,processes, and features be included within this description, be withinthe scope of the present invention, and be protected by the accompanyingclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings primarily are forillustrative purposes and are not intended to limit the scope of theinventive subject matter described herein. The drawings are notnecessarily to scale; in some instances, various aspects of theinventive subject matter disclosed herein may be shown exaggerated orenlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

FIG. 1 is a diagram illustrating the “calorie-in/calorie-out” (CICO)model of metabolism.

FIG. 2 is a diagram illustrating a blood glucose control model inaccordance with some embodiments.

FIG. 3 is a control block diagram illustrating the body's blood glucosecontrol system in accordance with some embodiments.

FIG. 4 is a graph illustrating the relationship between respiratoryquotient (RQ) values and the combustion of fats and carbohydrates inaccordance with some embodiments.

FIG. 5 is a diagram illustrating the relationship between RQ values andweight gain/loss in accordance with some embodiments.

FIG. 6 is an overall block diagram for an executable baseline metabolicmodel illustrating the relationship between food consumption, insulinlevels, body fat, blood glucose, etc., in accordance with someembodiments.

FIG. 7 is a block diagram of the internal composition of the glycogenstorage module in FIG. 6 in accordance with some embodiments.

FIG. 8A is a plot illustrating metabolic energy sources and glycogentransfer for a baseline energy-balanced case in accordance with someembodiments. FIG. 8B is a plot illustrating daily energy substrate andglycogen stores in accordance with some embodiments.

FIG. 9 is a plot illustrating stable adipose tissue stores for anenergy-balanced state in accordance with some embodiments.

FIG. 10A is a plot illustrating metabolic energy sources and glycogentransfer for the case in FIG. 8A but with high glycemic index inaccordance with some embodiments. FIG. 10B is a plot illustrating dailyenergy substrate and glycogen stores for the case in FIG. 8B but withhigh glycemic index in accordance with some embodiments.

FIGS. 11A and 11B are side-by-side comparison of days 7 and 8 for twodifferent glycemic index cases in accordance with some embodiments.

FIG. 12A is a plot illustrating change in fat storage resulting fromincreasing glycemic index of dietary carbohydrates in accordance withsome embodiments. FIG. 12B is a plot illustrating change in fat storageresulting from increased dietary intake of 300 kCal/day with nocompensating exercise in accordance with some embodiments.

FIG. 13A is a plot of change in fat storage resulting from increaseddietary intake of 300 kCal/day with fat burning exercise regimen of 225kCal/day in accordance with some embodiments. FIG. 13B is a plot ofchange in fat storage resulting from increased dietary intake of 300kCal/day with a carbohydrate burning exercise regimen of 225 kCal/day inaccordance with some embodiments.

FIGS. 14A and 14B are plots illustrating the results in carbohydrateintake and daily RQ average from a published carbohydrate overfeedingexperiment.

FIGS. 15A-15F are plots comparing published results from thecarbohydrate overfeeding experiment with results produced by a metabolicstate model simulation in accordance with some embodiments.

FIG. 16 is a cross-sectional view of an RQ device that includesprovision for measuring flow rate and hence energy expenditure, inaccordance with some embodiments.

FIGS. 17A-17G are perspective views of components of the RQ device inFIG. 6 in accordance with some embodiments.

FIG. 18A is a perspective view of an RQ device, also with provision fora flow-rate sensor, and FIG. 18B is a cross-sectional view of the RQdevice in FIG. 18A in accordance with some embodiments.

FIG. 19 is another perspective view of the RQ device in FIG. 18A inaccordance with some embodiments.

FIG. 20 is a process flow chart illustrating breath sampling inaccordance with some embodiments.

FIG. 21 is a process flow chart illustrating RQ device calibration inaccordance with some embodiments.

FIG. 22 is a process flow chart illustrating RQ measurement inaccordance with some embodiments.

FIG. 23 is a graph illustrating RQ measurements from a subject over timein accordance with some embodiments.

FIG. 24 is a graph illustrating (hypothetical) energy substrateutilization as it relates to speed/intensity of running in accordancewith some embodiments.

FIG. 25 is a graph illustrating energy utilization from carbohydrates intwo subjects while increasing speed on a treadmill in accordance withsome embodiments.

FIG. 26A is an image illustrating another RQ sensor prototype inaccordance with some embodiments. FIG. 26B is a series of representativeplots illustrating the breath-by-breath data and RQ obtained with thesensor shown in FIG. 26A in accordance with some embodiments.

FIG. 27 is an image illustrating components of a passive side-streamsampling system in accordance with some embodiments.

FIG. 28A is an image and FIG. 28B is a perspective wireframe view of amixing chamber for the passive side-stream system of FIG. 27 inaccordance with some embodiments.

FIG. 29A is a representative plot illustrating O2 and CO2 gasconcentration curves produced by a passive side-stream sampling sensorin accordance with some embodiments.

FIG. 29B is a representative plot illustrating respiratory quotient, andFIG. 29C is a plot of the corresponding breath volume rate and energyexpenditure estimates associated with the data from the passiveside-stream sampling sensor in accordance with some embodiments.

DETAILED DESCRIPTION

The number of people afflicted by obesity, diabetes and other metabolicdisorders has grown dramatically and alarmingly in the past decades,representing a major cost to society and adversely affecting the qualityof life of many. The present application describes a system model forhuman metabolism developed to gain insight into the relationship betweenthese metabolic disorders, their underlying causes, and to quantify therelationship between metabolic health and food. Furthermore, systems,apparatus, and methods related to modeling, monitoring, and/or managinga metabolic state of a subject are disclosed in the present application.

Existing metabolic models like the “calorie-in/calorie-out” (CICO) modelfail to capture the physiological complexity of metabolism and thereforedo not provide sufficient guidance to reverse obesity trends inindividuals or populations. Thus, a new metabolic state model isprovided for avoiding elevated blood glucose levels through properselection of dietary macronutrients and exercise sufficient to enablethe body's homeostatic system to achieve and maintain a healthy bodyweight. According to some embodiments, a personal respiratory quotient(RQ) measurement device and/or method is described for providingon-demand feedback indicative of a subject's real-time metabolic stateand/or guiding the subject's macronutrient intake and/or activity levelsto promote a desired metabolic state and/or achieve and maintain ahealthy body weight.

“Calorie-In/Calorie-Out” (CICO) Models

The use of calorie counting as a means to promote and maintain weightloss has gained popularity over the past several decades. While the coreconcepts driving the CICO model are based on the conservation of energyprinciple, many individuals who have followed a diet plan that is basedon the CICO model have met with failure to achieve and/or maintain theirweight goals. The CICO model does not provide any insight into themetabolic complexity associated with consumption of food and conversioninto useful metabolic energy or stored energy.

Specifically, the CICO model does not make a distinction regarding themacronutrient make-up of the food calories or the homeostatic mechanismsthat incite hunger or satiety in response to food and activity levels.For example, both one liter of soda and a salad provide about 450kilocalories, but a body's response to these two foods is different interms of how the calories are processed, how quickly the calories becomeavailable to meet energy needs, how much of a sense of satiety thecalories promote, and how much the calories perturb the blood glucoselevels from nominal.

Moreover, recent studies indicate that the CICO model cannot explain theprevalence of increased adiposity despite reduced energy intake (seeMozaffarian et al., NEJM, 364 (25): 2392-2404 (2011); Ludwig et al.,JAMA, 311 (21): 2167-2168 (2014)). For example, one longitudinal studythat included more than 120,000 subjects found that the kinds of foodconsumed affected the study participant's weight gain over a span offour years (id.). Another study proposed that diets that focus solely oncalorie intake, and specifically those that focus on the restriction ofcalories consumed, may increase underlying metabolic dysfunction and, asa result, increase hunger (id.).

While adherence to a calorie restricted diet can be effective inproducing weight loss, there are numerous reasons why calorierestriction usually fails to achieve and/or maintain long-term weightloss goals, including the following:

-   -   1) tracking food calories is time consuming and tedious and        consequently may be prone to error;    -   2) restricting food calories without regard to macronutrient        content can lead to nutritional imbalances that trigger hormones        and neural responses that result in overconsumption and/or an        inability to adhere to the calorie-restricted regimen;    -   3) genetic differences may affect a subject's basal metabolic        rates as well as a subject's response to exercise and/or        macronutrients, thus rendering formulaic prescriptive diets to        be, at best, approximations;    -   4) metabolic needs of a subject change with weight loss and        associated changes in the ratio of lean muscle mass to adipose        tissue, thereby requiring an individualized adaptive calorie        restriction formula to achieve a constant rate of weight loss or        maintenance of a reduced body weight; and    -   5) a scale—the most common method for tracking weight loss—is        effective in revealing long-term body weight trends but has a        high variance from measurement to measurement due to varying        states of the subject including, but not limited to, levels of        hydration, glycogen stores, and contents of the stomach,        intestines, and colon.

Aspects of an Optimized Metabolic State Model

According to some embodiments, a system model is premised on theobservation that the body places a very high priority on maintainingblood glucose levels in a narrow range. For an average size individual,normal blood sugar levels, on the order of 100 mg/dl, translate to acontrol objective of no more than 5 g of glucose circulating in thebody's entire blood volume. The fact that this level stays fairlyconstant, regardless of whether food is plentiful (hundreds of grams ofglucose coming in to the bloodstream per day) or non-existent, isevidence that the body places a high priority on maintaining normalblood glucose levels employing mechanisms to monitor blood sugar levels,methods to signal that it is out of range, and means of activelycontrolling and correcting blood glucose levels in response to thesesignals.

At the cellular level, these processing can be quite complex, however,even with no knowledge of the detailed metabolic pathways and processesthat enable these high level functions, the engineering discipline ofcontrol systems theory may be applied to develop a high-level systemmodel that captures the dynamic behavior of metabolic systems inresponse to food and levels of physical activity. By definition, asystem model is designed to “hide” underlying complexity which isaccomplished by incorporating only the essential functions necessary topredict the system behavior for the situations of interest. According tosome embodiments, a control system model enables assessment of thebody's ability to successfully control blood glucose, but moreimportantly, it also enables predictions of limitations of this control,and identification of situations in which the system is unable tosuccessfully maintain control.

Body weight is another quantity that the body seems to have the abilityto tightly control under some conditions, as evidenced by the fact thatmany people maintain nearly constant body weight over long periods oftime without having to consciously count and balance their caloriesconsumed and their calories expended. Rather than focusing on bloodglucose, a system model may be developed that describes the control ofbody weight in response to foods and exercise. However, body weightcontrol may be a lower priority than blood glucose control and, as such,metabolic behavior is largely explained by the priority of blood glucosecontrol. In fact, a principal mechanism the body employs to controlblood sugar is to suspend any constraint on body weight as a means ofachieving tight control of blood glucose.

The present application develops the control system approach formodeling metabolism from a notional construct to an executable numericalmodel.

The body's demonstrated ability to tightly control blood sugar impliesthat the process is well suited to analysis and modeling techniquesdeveloped for feedback control systems. FIG. 2 is a diagram illustratinga blood glucose control model 200 that considers the inputs of food intothe body and exercise by the body 202, the sensing of blood glucoselevel 204 by a blood glucose sensing system 206, which generates aglucose control signal 208, and a set of metabolic control functions 210that respond to the control signal and further inputs in accordance withsome embodiments. As illustrated in FIG. 2, in a feedback controlsystem, the value of the output parameter that is being controlled, inthis case blood glucose level 204, is measured and fed back into thecontroller, along with other external inputs, and the controllercontinuously adjust parameters in the system to achieve the desiredoutput.

Food and activity level represent the body's metabolic interface to theoutside world. Food intake, along with existing energy stores, providesthe fuel needed to sustain metabolic processes. As much as 70% of themetabolic energy required over the course of a day is devoted tosustaining autonomic processes such as breathing, circulating blood,digestion. In addition to these internal processes, individuals alsorequire energy to move about and perform daily activities.

The foods can place tremendous demands on the blood sugar controlsystem, since an influx of food may release large amounts of glucoseinto the bloodstream. In contrast, intense exercise may deplete largequantities of blood glucose, which must be replenished quickly tosustain critical processes such as brain function. In either case, inthe context of a blood glucose control model, the key parameterassociated with food intake and activity level, is the rate at whichglucose calories appear or are depleted from the blood stream.

Nutrients can have a direct effect on the flow of glucose into the bloodstream. The three main macronutrients (fat, carbohydrates, and proteins)each have different effects on the dynamics of glucose level in theblood. Fat is neutral to the rate of appearance of glucose, sincedigestion of fat does not result in any glucose being produced. Incontrast, the calories absorbed from carbohydrates are all destined tobe released into circulation in the form of glucose. The rate of glucoseappearance in the blood depends heavily on the timing of consumption,the quantity of carbohydrates consumed, and the glycemic index (ameasure the speed with which food is turned into circulating glucose)associated with the particular carbohydrate. Protein can also affectblood sugar, since it can be converted into glucose through the processof gluconeogenesis.

For typical modern diets, the glucose load into the bloodstream islargely driven by digestion of carbohydrates. For example, drinking asugary beverage might deliver 50 grams of glucose into the bloodstreamat rates as high as about 6-7 grams/minute. If this were not compensatedfor in some way (see actuators below), blood sugar levels would rise toover 1000 mg/dl from this one drink. The fact that high glycemic indexcarbohydrates can release glucose into the blood stream quickly setsdemand on the speed of response of the control system to mitigate theeffect.

Understanding the timing, quantity, and intensity of physical activity,whether it is part of daily living or formal exercise is essential tocorrectly model the ability to control blood glucose levels. The demandsthat physical activity places on the blood glucose control system aredirectly related to the rate of depletion of glucose they induce. Lowintensity exercise that burns fat will have negligible effects onglucose control; however, high intensity exercise utilizes glucose, andwill have a direct effect on the level of glucose in the blood.

Exercise can use glucose at rates of over 1000 kcal/hour, which is onthe order of 250 g of glucose per hour. Some of this fuel comes directlyfrom glycogen stores but still has important impacts on the function ofthe control system.

During eating or exercise, the blood glucose sensing function of thebody will detect changes in the glucose level and will respond bysignaling the body to act in a way to offset this change. Understandingthe details of how insulin and its counterpart, glucagon, broadcasttheir control signals to the body is not essential to correctly modelingthe functional behavior of the control loop. It is sufficient to knowthat subsystems in the body respond to increasing and decreasing bloodglucose levels by increasing and decreasing the level of the insulin andglucagon control signals.

Adopting control system terminology, in accordance with someembodiments, the mechanisms by which the body responds to the glucosecontrol signals may be referred to as “actuators.” According to someembodiments, four mechanisms may impact the quantity of glucose incirculation. In each of these functions, it is important to consider howmuch capacity they have to offset the rates of appearance anddisappearance of glucose in the bloodstream. These capacity limitsrepresent a nonlinear element of the control system and the effect ofthe nonlinearity is to produce complex dynamic responses despite arelatively small number of functional elements in the overall system.These nonlinear effects are critical to the understanding of type 2diabetes and obesity. TABLE 1 below summarizes the four mechanisms,assesses the rate of glucose control that they can achieve, and catalogsthe limit (capacity) of their control authority. For each of these fouractuators, it is noteworthy that there is a direct connection betweenthe presence of insulin and the rise in the level of the actuation,though this confirmation of insulin's integral role in affecting glucosecontrol is not required for this model to be valid.

TABLE 1 Known Glucose Response Control Actuation Control Capacity ToMechanism Method Rate Limit Insulin Increase Burn Glucose Moderate MaxYes Metabolic to lower level (50 g/hr) metabolic Rate rate (50 g/hr)Store Glu- Move glucose Very high Glycogen Yes cose as out of blood (250g/hr) stores Glycogen (~400 g total) Reduce Fat Increase glucose High 0%fat use Yes Burning burning for fuel (150 g/hr) (50 g/hr) Convert Removeglucose Moderate DNL rate Yes Glucose to by conversion (30 g/hr) inLiver Fat (30 g/hr)

“DNL” is used in TABLE 1 and throughout the present application as anacronym for de novo lipogenesis, which is described further herein.

The exact behavior of each of these mechanisms, as well as theircapacity limitations, will vary across individuals and may also changewith time for a given individual and are also a function of the recenthistory of food intake and exercise.

In response to a signal that blood sugar is high, the body can react byincreasing the basal metabolic rate to consume more glucose from thebloodstream. Even when an individual is not intentionally physicallyactive, they are still burning many calories for basal functions, asmuch as 70% of the total calories per day. These calories will draw fromthe fuel available in the bloodstream, and will serve to reduce thelevel of glucose in the blood. If, in response to the control signal,the metabolic rate increases, the rate of glucose consumption willincrease producing the desired effect in response to the control signal.The increase in metabolic energy level may be achieved through acombination of increased temperature, body motion, or other metabolicfunction. For the purposes of the model, it does not matter whatexplains the increase in rate only that it occurs and is subject to rateand capacity limits. The metabolic rate cannot be arbitrarily increasedin response to the signal, so its control authority to remove glucose islimited to on the order of 50 g/hour (basal rates of ˜2500 kCal/day).

The body has the ability to respond to the control signal by storingglucose in a place other than the bloodstream. The body stores glucosein the form of glycogen in the liver and in skeletal muscle distributedthroughout the body. While the exact behavior of the two storage formsare different, from the perspective a control system model, movingglucose in and out of storage represents a powerful tool for managingthe rates of glucose appearance and disappearance in the blood. It hasthe capability to act very quickly (perhaps 250 g/hour), though thetotal storage capacity is typically about 400 grams. Once the storagebecomes full, it can no longer sink excess glucose at any rate.Likewise, if glycogen stores are empty, they cannot be the source ofglucose to compensate for declining blood glucose levels.

The body can meet its metabolic fuel needs by using any availablenutrients. When using fat as a nutrient, it has no effect on bloodglucose level. When using glucose, it directly depletes the glucoselevel in the blood. Therefore, shifting the fuel mix towards glucose isanother control mechanism the body can use to respond to the controlsignal indication of elevated blood glucose. By adjusting the fuel mix,the body can quickly make large swings in the rate of glucosedisappearing. Of course, it cannot drive the mix beyond the extremes of0% or 100%, which represent a hard limit on the employment of thisactuator and in fact since the brain/blood barrier prevents fat fromentering the brain, some amount of glucose is necessary to sustain brainfunction and explains in part why blood glucose control is a priority.

The body modulates the level of fat burning by converting fats from freefatty acids (which can be used as fuel) to triglycerides (which cannot).Understanding this specific mechanism is not necessary to implement thecontrol model.

Finally, the body has the capacity to convert glucose into fat, aprocess known as de novo lipogenesis. This process removes glucose fromthe blood thereby lowering blood glucose. There is some debate as towhat rate de novo lipogenesis can occur at, and under what conditions itoccurs. It appears to activate as a last resort to blood glucose controlwhen the control authority of the three other mechanisms described abovebecome saturated.

According to some embodiments, a metabolic state model is optimizedbased on avoiding elevated blood glucose levels through proper selectionof dietary macronutrients and exercise sufficient to enable the body'shomeostatic system to achieve and maintain a healthy body weight.

These inputs, the sensing function, control signal, and the actuatorsdescribed above may be combined in a way that shows their connectivityand allows explanation of their interactive function. FIG. 3 is acontrol block diagram illustrating the body's blood glucose controlsystem in accordance with some embodiments. In FIG. 3, food calories 302from carbohydrates 304 and fats 306 are input into the model.Carbohydrates 304, in the form of glucose, in the circulation aredepicted flowing from left to right, and dietary fat 306 is depicted asflowing from left to right into fat storage or adipose tissue 308. Forfat, model 300 is extremely simple. Since the body has essentiallyunlimited capacity to store fat, the disposition of dietary fat can bemodeled by initially placing all consumed fat into this vast storagereservoir. According to some embodiments, the fat storage may be treatedas unlimited and/or the fat storage may be allowed to contribute tometabolic needs. For dietary carbohydrates, storage options are limited,and carbohydrates are depicted flowing directly into the bloodstream, ata rate determined by the glycemic index, raising the blood glucose level310. Excess carbohydrates in the circulation may be temporarily storedas glycogen 312 in the liver and the skeletal muscles. Excesscarbohydrates in the circulation also may be used to fulfill metabolicneeds 314. For example, metabolic needs may be met with energy fromcarbohydrates 316 and/or energy from fat 318. Further excesscarbohydrates in the circulation may result in de novo lipogenesis 320.

According to some embodiments, the metabolic state model acts tomaintain a constant blood glucose level 310. For example, a healthyadult human regulates blood glucose levels within a narrow band ofbetween about 80 to 110 mg/dl×50 dl (i.e., about 5 g or about 20calories). This blood glucose level is roughly constant whether food isplentiful or scarce. If carbohydrates are not temporarily stored asglycogen 312, used for metabolic needs 314, or converted into fat 320,β-cells in the pancreas 322 will produce the hormone insulin 324 to helpcontrol blood glucose.

According to some embodiments, the metabolic state model identifies thecritical metabolic state control loops or actuators, depicted in FIG. 3as independent valves 326, 328, 330, and 332. The master regulator foreach valve is insulin. The pancreas 322 monitors the level ofcirculating glucose 310, and emits insulin 324 as a control signal tofour actuators for lowering blood glucose. Each of the actuators isshown in the figure as a valve that responds to the control signal. Theglycogen storage valve 326 shuttles glucose into the limited stores ofglycogen 312, the metabolism valve 328 controls the metabolic rate 314,the fat burning valve 330 turns off to maximize the use of glucose, andthe lipogenesis valve 332 ramps up de novo lipogenesis 320.

According to some embodiments, these metabolic state control mechanismsinclude:

-   -   a) burning more glucose for energy through intentional increase        in activity (e.g., exercise) or unintentional        homeostatically-induced responses (e.g., fidgeting);    -   b) storing more glucose as glycogen in the liver and skeletal        muscles (facilitated by depleting glycogen storage, e.g.,        through exercise);    -   c) blocking the use of dietary fat as an energy source (i.e.,        store the dietary fat instead so that metabolic needs require        more glucose); and    -   d) converting more glucose into fat (i.e., de novo lipogenesis).

According to some embodiments, any or all of these responses may be rateand capacity limited. These rates and capacities may be adjusteddepending on factors including, but not limited to, the species, gender,age, and genetic factors associated with each individual subject. Insome embodiments, metabolic needs are capped at, for example, about 1500calories per day depending on the individual subject. The glycogenstorage capacity in an adult human is approximately 15 g/kg of bodyweight. Thus, in some embodiments, glycogen storage is capped at, forexample, about 1400 calories depending on the individual subject. Insome embodiments, de novo lipogenesis is capped at, for example, about300 calories per day depending on the individual subject.

Also shown in this model is the notion of glucose overflow 334. Whenblood glucose exceeds a subject's homeostatic mechanisms by reaching alevel at which the excess glucose cannot be disposed of through acombination of combustion, conversion or storage, then other mechanismsmanifest themselves in response. The glucose will overflow into thesurrounding tissues (e.g., accumulating in tissues, joints, retinas,etc.) and the kidneys will pass glucose into urine, which ischaracteristic of type 2 diabetes. These are not considered as actuationmethods above, since they represent disease conditions.

In some embodiments, the metabolic state model provides insights intoand feedback regarding the relationship between chronically elevatedblood glucose levels and obesity.

Diabetes is defined as the impaired ability to control blood glucose, sothis model has direct applicability to understanding its behavior. Fortype 1 diabetes, the impairment is in the sensing system. Despite theelevation of blood glucose, no signal, or a very weak one, is producedto signal the actuation mechanisms. Whether the missing signal is causedby an inability to sense glucose in the blood or an inability to produceinsulin to broadcast the message, the actuators are never enableddespite having ample capacity to control blood sugar. When insulin ismanually added to the blood stream, the full set of actuation mechanismswill properly function to control blood glucose levels, thoughmaintaining the precise level of insulin required to balance incomingglucose with the setting of the actuators is very difficult to achievewithout constant monitoring of the blood glucose level. This model maybe employed to better understand the dynamics of the insulin controlsystem, in much the same way as an artificial pancreas might function.

In some embodiments, this model is also particularly useful in providinginsights into type 2 diabetes. In this disease state, the body is alsounable to control blood glucose levels; however, it is not due to amalfunction of the sensing portion of the control system. Typicalinsulin levels with type 2 diabetes are much higher than normal, yet theblood glucose control system does not adequately respond to the sensorsignal. The condition is referred to as insulin resistance, suggestingthat the actuation mechanisms need higher signal levels than previouslyrequired to reach the proper control level. However, there is not aclear understanding or consensus regarding the root cause of thisapparent loss in insulin sensitivity.

In some embodiments, this model may offer an alternative explanation ofthe precursors and root cause of type 2 diabetes. If all of the fouractuation mechanisms described above are functioning at their fullcapacity, then the presence of higher levels of insulin will haveminimal effect on the rate at which glucose is cleared from the blood,and all of the symptoms of type 2 diabetes will be present, despite highlevels of insulin. Blood glucose control will fail if the controlauthority available in the actuators is less than the rate of glucoseappearance in the blood stream. This interpretation of type 2 diabetesimplicates the macronutrient content we ingest as the root causediabetes symptoms. Any individual whose actuation mechanisms cannot keepup with the rate that carbohydrates are being absorbed will exhibitdiabetes symptoms. Changes to diet that add carbohydrates and that movetoward high glycemic index foods will challenge the available controlauthority of a greater fraction of our population. This explanation oftype 2 diabetes does not require postulating or explaining the originsand mechanism of insulin resistance. Conversely, the model suggests thatif carbohydrate rates of appearance are reduced to be compatible withthe individual's control authority to process carbohydrates, then thesymptoms of type 2 diabetes may quickly disappear.

The genesis of the metabolic model was to functionally model themechanisms involved in responding to changes in blood glucose. However,the insight the model provides may extend beyond understanding just thecontrol of blood glucose, by quantifying how the blood glucose controlsettings affect body weight.

Contrary to the view that obesity is the cause of insulin resistance andthe onset of type 2 diabetes, this model suggests an alternativesequence of events according to some embodiments. For example, the modelpredicts that when the body is struggling to reduce high blood glucoselevels, it will shut off the burning of fat and in some situationsconvert excess glucose into fat in an attempt to keep up with the rateof glucose input. If, as a result of dietary habits, an individual isconstantly releasing high levels of glucose into the blood stream, theresponse of these control mechanisms will manifest as an increase instored fat. Consequently, for an overweight individual seeking to reducebody weight through a low fat (hence high carbohydrate) diet, themetabolic settings the body is choosing in an effort to control glucoseprevent the fat burning needed to reduce body weight.

In some embodiments, the model may be used to predict an individual'sresponse to a given diet and how the response varies with number ofcalories, macronutrient mix, glycemic index, duration of exercise,and/or intensity of exercise. Tuned to an individual, the model may beused to determine what dietary intake and nutrient mix will ensure thatblood glucose control does not override the body's signals related toweight control, enabling the individual to burn dietary and stored fat,rather than carbohydrates, to meet metabolic energy needs.

In some embodiments, described further herein, a fully numericalsimulation of the control system of FIG. 3 may be used to producequantitative assessments of how diet and exercise affect diabetes andobesity. In further embodiments, also described further herein, anapparatus comprising a metabolic sensor may enable an individual toeasily customize this model to their own metabolic limits and behavior,as well as obtain on-demand feedback on how hard the body is working tokeep blood glucose in the normal range.

The metabolic state of a body (e.g., a fat burning or a de novolipogenesis state) can be accurately tracked by measurement of therespiratory exchange ratio (RER). As used herein in the specificationand in the claims, RER is interchangeable with respiratory quotient(RQ). Both RER and RQ are measurements of the ratio of carbon dioxide(CO₂) production and oxygen (O₂) consumption.

FIG. 4 is a graph illustrating the relationship between respiratoryquotient (RQ) values and the combustion of fats and carbohydrates inaccordance with some embodiments. RQ measurements may inform whether asubject is primarily in a carbohydrate-burning state (RQ≅1.0), in amixture of carbohydrate and fat-burning state (e.g., RQ≈0.85), orprimarily in a fat-burning state (RQ≅0.7). Note that RQ values between1.0 and 0.7 represent a mixture of fat, carbohydrate, and proteinburning states. During rest and low activity levels, RQ values in excessof 1.0 indicate that a subject is turning carbohydrates into fat (i.e.,de novo lipogenesis state).

FIG. 5 is a diagram 500 illustrating the relationship between RQ valuesand weight gain/loss in accordance with some embodiments. RQmeasurements may inform whether a subject is in a state that contributesto weight gain 502 (RQ≥1.0), maintains weight 504 (e.g., RQ matched tothe food quotient, nominally 0.85), or contributes to weight loss 506,with RQ near the low end of the scale (RQ≅0.7) indicating a highpercentage of metabolic energy derived from fat.

RQ is a measurement of the ratio of the volume of carbon dioxideproduced per unit time to the volume of oxygen consumed per unit timeand may be calculated using equation (1) below:

$\begin{matrix}{{RQ} = \frac{{V{CO}}_{2}}{{VO}_{2}}} & (1)\end{matrix}$

RQ can be used to represent when a subject is in a carbohydrate burningstate or in a fat burning state. For example, the carbohydrate burningstate may be represented as chemical equation (2) below, which yieldsthe RQ value of 1.0 according to equation (3) below:

$\begin{matrix}\left. {{C_{6}H_{12}O_{6}} + {6O_{2}}}\rightarrow{{6{CO}_{2}} + {6H_{2}O}} \right. & (2) \\{{RQ} = {\frac{{V{CO}}_{2}}{{VO}_{2}} = {\frac{6}{6} = 1.0}}} & (3)\end{matrix}$

The fat burning state may be represented as chemical equation (4) below,which yields the RQ value of 0.7 according to equation (5) below:

$\begin{matrix}\left. {{C_{18}H_{36}O_{2}} + O_{2}}\rightarrow{{18{CO}_{2}} + {18H_{2}O}} \right. & (4) \\{{RQ} = {\frac{{V{CO}}_{2}}{{VO}_{2}} = {\frac{18}{27} = 0.7}}} & (5)\end{matrix}$

According to some embodiments, the incorporation of RQ measurements in ametabolic state model provides advantages over food calorie tracking andrestriction by directly assessing the precise metabolic state of asubject on demand. Unlike calorie-based weight management, coupled withbody weight tracking, RQ provides immediate feedback to a subject as towhether or not diet and exercise activities, which may be modulated bygenetic factors unique to each individual, are putting the subject in ahomeostatic zone where body weight and glucose are both successfullycontrolled. Specifically, when a subject's glucose system has beenoverwhelmed for long periods of time (indicated by blocking of fatburning, and conversion of excess glucose into fat via de novolipogenesis), the subject's body manifests these metabolic responses inthe form of weight gain. RQ measurements may be used to avoidoverconsumption of carbohydrates, enabling a subject to maintain normalblood glucose levels thereby allowing weight-related homeostasismechanisms to properly function and, as a consequence, lose or maintainweight. In addition to weight control, RQ measurements also may beuseful in early detection, prevention, and/or management of type 2diabetes and/or other metabolic diseases. Additional uses of RQmeasurements include, but are not limited to, supporting athletictraining and assessing athletic endurance.

The invention will be further described in the following examples, whichare not intended to limit the scope of the claims.

EXAMPLES An Executable and Optimized Metabolic State Model

An executable state model optimized for metabolism is disclosedaccording to some embodiments.

Inputs into the executable metabolic state model may include, but arenot limited to, one or more RQ values, a macronutrient composition andcaloric value of food consumed by the subject (e.g., carbohydratesversus fats), an intensity and duration of activity by the subject(e.g., carbohydrate-burning activity versus fat-burning activity), arate and maximum capacity of glycogen storage in the subject, a rate andmaximum capacity of de novo lipogenesis in the subject, and/or a qualityand duration of sleep by the subject. In some embodiments, an executablemetabolic state model is run on an RQ device and/or communicativelycoupled with an RQ device. Additional variables may include, but are notlimited to, information associated with measures of blood glucose,glycogen, average carbohydrate intake, basal carbohydrate metabolism,body fat, average fat intake, basal fat metabolism, body weight, heartrate, respiration rate, average insulin level, and/or insulin level inthe subject.

Outputs from the model may include, but are not limited to, rawmeasurement values and/or feedback in the form of suggested nutritionaland/or exercise modifications or goals relating to weight management,disease management, and/or athletic training.

According to some embodiments, the numerical model reveals the timeresponse behavior of metabolism to both food and activity levels and,consequently, enables investigation of the impact of dietarymacronutrients and exercise on the selection of energy substrate and thecontrol of body weight. An executable model may require quantifying howeach of the four control methods described respond to the presence ofthe control signal (e.g., an “insulin” control signal, which takes onpositive values in response to high blood glucose levels and negativevalues in response to low blood glucose levels, with the negative valuesrepresentative of glucagon signaling), and how the magnitude of theresponse becomes saturated as it reaches its control limit. In someembodiments, a numerical description of how the insulin control signalitself is generated in response to changes in blood glucose level isdeveloped.

With simple descriptions of these component pieces, hardware and/orsoftware tools, such as Simulink® (available from MathWorks®, Natick,Mass.), which is built on the idea of signal flow graphs, may be used tosimulate the model behavior when all of these pieces interact. FIG. 6 isan overall block diagram 600 for a baseline metabolic model inaccordance with some embodiments. The simulation allows for inputs offood and exercise, a model of each of the four mechanisms to processcarbohydrates, fat stores behavior, an insulin secretion model, andmonitors of key parameters.

Many variations and extensions to the baseline executable model of FIG.6 are possible. For example, Simulink® includes an extensive library offunctional blocks that may be specialized and interconnected torepresent the information flow associated with the processes ofinterest. In FIG. 6, four functional block types are depicted inaccordance with some embodiments. One functional block type is a pulsegenerator 602, which is used in the metabolic model to generate periodicfeeding or exercise schedules. For example, pulse generator 602 arepresents carbohydrate burning exercise, and pulse generator 602 brepresents fat burning exercise. A second functional block type is asumming junction 604. A third functional block type is an integrator 606with upper and lower saturation limits. A fourth functional block typeis a first order transfer function 608, with time constant 1/a, which isused in the metabolic model to generate daily averages. Viewing scopes610 are used in the metabolic model to plot time evolution of one ormore user-selected parameters, including basal carbohydrate metabolism,basal fat metabolism, body fat, glycogen level, total blood glucose, andaverage insulin.

In FIG. 6, five modules are depicted in accordance with someembodiments. Each module may be a multi-input and/or multi-outputuser-defined subsystem, which further includes a number ofinterconnected functional blocks. Module 612 executes food consumptionfunction(s), module 614 executes de novo lipogenesis function(s), module616 executes glycogen storage function(s), module 618 executesmetabolism function(s), and module 620 executes pancreas function(s).Each module is simply a means of grouping together functional blocksthat together implement a common function. More or fewer modules, evenno modules, may be used in accordance with some embodiments.

The food consumption module 612 as well as two exercise blocks 602 a,602 b together provide the dietary intake and activity levels over theduration of the simulation in accordance with some embodiments. Withinmodule 612, the time for each meal, calorie, and macronutrientcomposition is specified. The carbohydrate burning exercise block 602 ais used to represent high intensity, carbohydrate burning activities,while the fat burning exercise block 602 b is used to represent lowerintensity, fat burning exercise, such as walking. The blocks may beemployed together to represent any combination of fat plus carbohydrateburning exercise. Employing the pulse generators 602 to representexercise allows the duration and frequency of the exercise to bespecified as well as the phasing or time of day for the start ofexercise. The exercise model can be made as complex and variable fromday-to-day as desired, but in the example of FIG. 6, is modeled as afixed duration at the same time each day.

The de novo lipogenesis module 614 in FIG. 6 implements de novolipogenesis based on the insulin levels and available glycogen storagecapacity in accordance with some embodiments. When the insulin levelsare high, implying high blood glucose, and there is little or noremaining glycogen storage capacity available, de novo lipogenesis isinitiated to begin converting circulating blood glucose to fat forstorage. De novo lipogenesis fat is added to any dietary fat and thecombined signal is reduced by any fat burning exercise and heightenedmetabolic rate before being added to the body fat stores, which can beviewed on the body fat viewing scope.

The dietary carbohydrates take a slightly more complicated path throughthe model in FIG. 6, passing through three summing junctions where thedietary intake can be reduced by de novo lipogenesis activity, glycogenstorage, and metabolic rate, before passing through an integrator thattracks the blood glucose level, subsequently passing this signal to thepancreas module 620 where the insulin control signal is generated basedon the blood glucose levels.

The glycogen storage module 616 controls the glycogen storage andretrieval process which is modeled as a whole-body effect, with nodistinction between glycogen storage in liver versus muscle tissue. Thisdistinction may be incorporated in the module if desired.

FIG. 7 is a block diagram of the internal composition of the glycogenstorage module 616 in FIG. 6, which is described in detail to provide arepresentative example of the internal structure of a functional moduleaccording to some embodiments. In FIG. 7, some new functional blocktypes are depicted, including a signal input port 702 for a user definedmodule, which is used in the glycogen storage module 616 to inputcarbohydrate exercise rate 702 a and insulin level 702 b. Another newfunctional block type is a gain block 704, which is used in the glycogenstorage module 616 to multiply an incoming signal by a user-defined gainterm K. Yet another new functional block type is a signal output port706 for a user defined module, which is used in the glycogen storagemodule 616 to output glycogen transfer from blood 706 a and glycogenlevel 706 b.

If insulin level 702 b is positive, insgain/glycap block 704 is scaledby the fraction of remaining glycogen storage capacity (i.e.,(glycap-glylevel)/glycap), with the result that insulin impact onglycogen storage varies from a maximum of insgain, when glycogen storesare fully depleted, to zero, when glycogen storage is at capacity.

If insulin level 702 b is negative (i.e., glucagon), insulin impact onglycogen retrieval varies from insgain to zero in proportion toglylevel/glycap. The function of these blocks comprising the glycogenstorage module 616 and their ranges are summarized in TABLE 2 below.

TABLE 2 Element Units Range Default Input: Insulin Level — Defined ininsulin ±30 module Input: Carb Exercise Rate Kcal/hr Defined in carb 0exercise module Gain2: insgain/glycap kcal⁻¹ Set in module 50/2300 maskparameters Switch: controlled by insulin kcal ±insgain — polarity Store1: Integrator Output kcal 0 to glycap 0.5*glycap Store 1: Saturationindicator — 0 unsaturated, — ±1 when saturated Output: Glycogen Level —0 to 1 0.5 fraction Output: Glycogen Transfer kcal/hr insgain* 50*30(insulin level)

Turning attention again to the overall metabolic model block diagram inFIG. 6, the metabolism module 618 monitors the glycogen storage leveland the insulin level in order to adjust the fraction of metabolicenergy derived from carbohydrates versus fats, with the ability tocompletely shut off fat as a source of energy and rely exclusively oncarbohydrates (glucose). The metabolism module 618 may include auser-settable basal metabolic rate with diurnal variation and also asthe capability to increase the basal metabolic rate over auser-specified range in response to high levels of insulin.

For anyone familiar with the cellular level processes for supplyingmetabolic energy demands, storing excess carbohydrates and fats andreleasing the hormones necessary to control these processes, theelements comprising the block diagram in FIG. 6 may seem too sparseand/or simple to provide useful insight into these complex metabolicprocesses. However, while the model is simple by design, the output ofthe model can be quite complex and varied in response to dietary andexercise inputs. For example, the model may be shown to capture thesalient metabolic responses identified in a comprehensively documentedfourteen-day study of carbohydrate overfeeding with reasonable accuracyas described further herein.

The performance of the model and the function of the modules in FIG. 6may be illustrated with a simple baseline reference case andmodification of the food and exercise inputs.

For example, in a baseline case, the basal metabolic rate in themetabolism module 618 is set to 1800 kCal/day, with a diurnal variationof ±300 kcal, and the feeding schedule consists of breakfast, lunch, anddinner with no between-meal snacks. The macronutrient mix and calorieequivalent of the meals is shown below in TABLE 3.

TABLE 3 Parameter kcal Carb % τ_(FAT) (hrs) τ_(GLY) (hrs) Breakfast  55056 0.5 0.2 Lunch  500 56 0.5 0.2 Dinner  775 56 0.5 0.2 Total or Average1825 56 0.5 0.2

Note that for this baseline example, daily dietary intake is just 25kcal higher than the basal metabolic rate and all of the meals arecomprised of 56% carbohydrates and 44% fats with the absorption timeconstants for the carbohydrates and fats, different from one another butconstant over all meals. These parameters represent a nearlyunrealizable uniformity for actual meals but serve to illustrate thesalient features of the model. For simplicity and clarity ofillustration, the meal schedule and composition is held fixed for eachsimulation day.

In the reference baseline, there is no exercise or physical activityscheduled other than the diurnal basal metabolic rate of 1800 kCal/day.The available fat stores at the beginning of the simulation areinitialized to 25,000 kcal which, at a nominal 3500 kcal/lb correspondsto approximately 7 lb of adipose tissue. The glycogen storage capacityis initialized to 2300 kcal, the equivalent of 575 g of carbohydratestored in the liver and muscles, with glycogen stores half full at thebeginning of the simulation.

According to some embodiments, the metabolic model is run for asimulation time period of two weeks, or 336 hours, and completesexecution in less than about one second. The seven view scope iconsdepicted in FIG. 6 represent viewers in which the parameter identifiedcan be viewed. Additional scopes may be added to display and comparemultiple parameters in one plot.

FIG. 8A is a plot illustrating metabolic energy sources and glycogentransfer for the baseline energy-balanced case in accordance with someembodiments. In particular, FIG. 8A depicts the change in glycogenstores and the energy derived from carbs and fats in units of kcal/hr.The impact of the three meals is evident in the daily spikes in bloodglucose and metabolic activity. There is also movement of carbohydratesin and out of glycogen storage although the amounts are small.Integration of the curves results in the daily averages shown in FIG.8B.

FIG. 8B is a plot illustrating daily energy substrate and glycogenstores in accordance with some embodiments. In particular, FIG. 8Bpresents this information in the form of a daily average of carbohydrateand fat energy sources along with the glycogen storage all in kCal/day.After the initial transient associated with initializing the model, theaverage daily energy expenditure approaches a steady state matched tothe dietary intake, with small transients related to the three dailymeals, carbohydrates moving in and out of storage, and diurnal variationin the basal metabolic rate.

The implication is that there is an energy balance which, as FIG. 9illustrates, results in a stable value of stored fat over the simulatedtwo week time period. FIG. 9 is a plot illustrating stable adiposetissue stores for energy balanced state in accordance with someembodiments.

In the baseline case, the dietary intake is closely matched to theresting metabolic rate and the glycemic index of the diet is low,thereby minimizing blood glucose spikes and resulting in a stable weightand good control of blood glucose. In the next example, the impact onmetabolism is assessed from simply increasing the glycemic index of thediet, from 0.2 to 0.6, without changing either the total dietarycalories or macronutrient mix. The increase in glycemic index by afactor of three results in carbohydrates entering the blood stream threetimes more quickly, spiking the blood sugar, generating a strong insulinresponse and more forcefully activating the glucose control mechanisms.

FIG. 10A is a plot illustrating metabolic energy sources and glycogentransfer for the case in FIG. 8A but with high glycemic index inaccordance with some embodiments. Relative to FIG. 8A, blood glucoseexcursions are larger as are the glycogen transfer rates andcarbohydrate metabolism. Relative to the low glycemic index baselinecase, FIG. 10A clearly exhibits more rapid temporal variation consistentwith a higher glycemic index in FIG. 8A, and larger excursion fromnominal or average levels for all of the signals shown. FIG. 10B is aplot illustrating daily energy substrate and glycogen stores for thecase in FIG. 8B but with high glycemic index in accordance with someembodiments. Relative to FIG. 8B, the peak carbohydrate metabolism isincreased, the average fat metabolism is reduced and glycogen storagevariability is increased.

To better compare the two cases and understand the impact of the highglycemic index, FIGS. 11A and 11B are side-by-side comparison of days 7and 8 for the two different glycemic index cases in accordance with someembodiments. Referring to FIG. 11A, corresponding to the low glycemicindex case, the glycogen transfers, which are in units of kcal, arerelatively small, on the order of 40 kcal (10 g) or less. The bloodglucose level rises after each meal from the baseline set point of 30kcal to a peak of around 40 kcal. There is little evidence ofhypoglycemia. Note that while the dinner meal has the highest fatcontent, the fat metabolism, in units of kcal/hr, exhibits the lowestpeak for the dinner meal because priority has been given to metabolizingblood glucose from the carbohydrate content of the meal and moving someof the excess blood glucose into glycogen storage.

In FIG. 11B, corresponding to the high glycemic index case, the bloodglucose excursions are larger, peaking at 66 kcal with lows less than 13kcal, indicative of hypoglycemia, and the glycogen transfer excursionsare also larger with 110-kcal storage peaks and 30-kcal retrieval peaks.The fat metabolism is suppressed relative to the low glycemic case sinceas the as the blood sugar spikes due to the higher glycemic index,carbohydrate metabolism peaks and reliance on fat to meet metabolicenergy needs is suppressed. At the same time more of the dietarycarbohydrate intake is being moved into glycogen storage. These actionsare all driven by higher levels of circulating insulin. At thecompletion of carbohydrate digestion blood glucose rates fallprecipitously since there is no longer a source of glucose but thecirculating blood insulin level does not return to normal immediatelyleading to episodes of hypoglycemia. In a real situation rather than asimulation, these hypoglycemic events might lead to additionalcarbohydrate snacking in response to the low blood glucose. However, inthe simulation, the feeding schedule is prescribed (which one mightthink of as a dieter with an “iron will” or a clinical study in whichdiet is strictly controlled).

FIG. 12A is a plot illustrating change in fat storage resulting fromincreasing glycemic index of dietary carbohydrates while preserving thebaseline dietary caloric intake in accordance with some embodiments.Total increase in stored fat over the two-week simulation is small(1000-kcal equivalent to a weight gain of about one-quarter pound) butis indicative of an energy imbalance induced solely by a change inglycemic index. Since the metabolic response to the blood sugar spikesis to increase reliance on circulating glucose to meet energy needswhile reducing reliance on circulating fat, the net result, as shown inFIG. 12A, is that body weight is increased as dietary fat moves intostorage during episodes of high blood glucose. As a response to thesubsequent episodes of hypoglycemia, glycogen, rather than the dietaryfat, is moved out of storage to counteract the resulting low bloodglucose.

Since the baseline model exhibited an energy balance, with a basalmetabolic rate of 1800 kCal/day and a food intake of 1825 kCal/day, onemight wonder why simply increasing the glycemic index would result inweight gain when the system was previously in energy balance. The answerto the apparent mystery of where the extra energy came from to produceweight gain has to do with one of the four control mechanisms identifiedin TABLE 1. In particular, as blood glucose levels rise, carbohydratemetabolism increases in order to dispose of glucose at a higher rate.The increased metabolism may take the form of higher body temperature,higher agitation, but, apart from actively increasing exercise, there isa limit to the total metabolic rate that can be achieved, which in thebaseline model considered here is set to 100 kcal/hr. In the baselineenergy-balanced example with low glycemic index, the maximum metabolicrate was never approached. However, with the higher glycemic index, themaximum metabolic rate needed to control the blood glucose is higherthan the allowable maximum which leads to higher rates of glycogenstorage and lower rates of fat metabolism resulting in higher levels offat storage. During the episodes of hypoglycemia, the metabolism adaptsto lower values and begins recruiting stored glycogen, not thepreviously stored fat, to raise blood glucose levels. The fat that wasstored during the blood glucose spikes remains in storage since it willnot help with blood glucose control. The overall cycle of the adaptivemetabolism rate hitting a maximum followed by a nadir, lowers theaverage metabolism over the course of the three meals and the reducedaverage energy demands account for the increase in body fat.

The previous examples did not include any explicit exercise periods. Tofurther demonstrate the efficacy of the model and the insights itaffords, the model is returned to the reference diet defined in TABLE 3,but 100 kcal are added to each meal of the three daily meals, increasingthe daily caloric intake from 1825 kcal to 2125 kcal. The glycogenstorage is also initialized to full capacity at the start of thesimulation, which may be representative of a sedentary individual. FIG.12B is a plot illustrating change in fat storage resulting fromincreased dietary intake of 300 kCal/day with no compensating exercisein accordance with some embodiments. As shown in FIG. 12B, without theintroduction of exercise, the increased calorie intake of 300 kCal/dayresults in a fat gain of 4090 kcal (about 1 lb) during the course of thetwo-week simulation.

A misconception sometimes promoted by fitness trainers is that “if youwant to lose weight, you should preferentially engage in low intensityexercise, such as walking, since it is fat burning.” Without question,low intensity exercise favors fat over carbohydrate oxidation. However,in terms of optimal weight loss, this advice ignores the complexinteractions of the metabolic subsystems and the priority placed onblood glucose control. In other words, without a quantitative model toguide understanding, it is easy even for the experts to be misledregarding the impact of diet and exercise choices. To illustrate, thesimulation is run again, this time adding exercise to offset the 300kCal/day increase in dietary intake. All of the exercise is first addedas low-intensity fat burning, and then the simulation is repeated withall of the exercise represented as high intensity carbohydrate burningexercise. For the purpose of comparison, the exercise is begun each dayat 6 am and is 1.5 hours in duration with a total calorie expenditure of150 kcal/hr. The 150 kcal/hr is not representative of high intensityexercise, but the same time duration and calorie rate for thecarbohydrate burning exercise are used to keep as many variables thesame for the sake of comparison and to focus attention on the impact offat burning versus carbohydrate burning. In terms of justifying thisassumption, one could imagine high intensity exercise with rest periodsinterspersed in order to achieve the same average kcal rate as the lowintensity exercise over the 1.5 hour duration.

FIG. 13A is a plot of change in fat storage resulting from increaseddietary intake of 300 kCal/day with fat burning exercise regimen of 225kCal/day in accordance with some embodiments. As shown in FIG. 13A, thefat burning exercise reduced the weight gain from 4090 kcal with noexercise over the two week simulation, down to 1390 kcal with fatburning exercise. Note however that the positive slope on the fataccumulation curve implies that additional gains will occur insubsequent weeks under this feeding and exercise regimen.

In comparison, FIG. 13B is a plot of change in fat storage resultingfrom increased dietary intake of 300 kCal/day with a carbohydrateburning exercise regimen of 225 kCal/day in accordance with someembodiments. As indicated in FIG. 13B, carbohydrate burning exercise ofthe same calorie value and duration as the fat burning exercise resultsin a peak fat gain of 1080 kcal on day 6, the weight gain then begins tofall off slightly to a stable 1020 kcal by the end of the second week.

The fact that the same level of energy expenditure leads to differentbody fat outcomes for fat burning versus carbohydrate burning exercisemay not seem surprising, but it does seem counter-intuitive that fatburning exercise would be the less effective exercise to achieve weightloss. This and the previous example clearly reveal the inadequacy of asimple calories-in/calories-out model of weight loss.

While the executable metabolic model of FIG. 6 behaves as expected forthe contrived examples described in the previous sections, a morerigorous evaluation of the model fidelity may require comparison withcarefully controlled studies in which the dietary intake, activitylevels, and metabolic state are monitored over one or more diurnalcycles. A challenge in validating the metabolic model in this way isthat few published studies provide a complete description the metabolicstate of the subjects at the beginning of the study or reportmacronutrient composition of meals and feeding and exercise schedules insufficient detail. For example, there is seldom knowledge of the stateof an individual's glycogen stores at the beginning or end of anexperiment, and the previous examples have shown that the state ofglycogen stores has a significant impact on how an individual respondsto a high carbohydrate diet.

From a modeling perspective, TABLE 4 below summarizes the desiredinformation for proper initialization of a metabolic model, according tosome embodiments, and identifies the corresponding parameters reportedin three of the more comprehensive studies uncovered in the literatureand incorporated by reference herein: Acheson et al., Am. J. Clin.Nutrition, 48:240-47 (1988); McDevitt et al., Am. J. Clin. Nutrition, 74(6):737-46 (2001); and Jebb et al., Am. J. Clin. Nutrition, 58:455-62(1993).

TABLE 4 Info Acheson McDevitt Jebb Documented Desired (1988) (2001)(1993) Duration Days to 3 + 7 + 2 4 7 + 12 (days) observe trends Numberof More is better 3 13 5 subjects Subject Gender, age, Young maleMiddle- Healthy, physiology BMI, fitness athletes aged women ages 19-42Body Measured Weight, Weight, Weight, composition at least daily BMI,BMI, BMI, % body fat DXA density Fat and Measured Daily 4-day Dailycarbohydrate daily aggregate balance Subject Measured N/A N/A 1 hour BMRdaily per day Initial Known state Depleted N/A N/A glycogen at startstores Energy Measured at Daily 4-day Daily expenditure least diurnallyaverage aggregate average Exercise Intensity and 3 per day 4 per dayErgometer duration 3 × 40 min Meal Time and 3 per day 4 per day N/Aschedule duration Meal Macronutrients, Daily mix, 4-day Macro-composition GI no GI aggregate nutrients, kcal Hydration Daily intakeN/A N/A N/A RQ Minimum Daily N/A Daily hourly average average DNL RQ and²H₂O Daily by ²H₂O trace to N/A rates RQ > 1 fat stores ProteinQuantified Quantified N/A Urinalysis metabolism through Blood plasmaurinalysis CGM, 7 × Triglycerides, N/A analysis insulin, leptin,triglycerides, insulin, triglycerides insulin, glucose, throughout dayglucose, leptin at lipoproteins 96 hours

For example, in Acheson (1988), a comprehensive fourteen-day metabolicenergy balance study included a nearly complete description of theparameters necessary to establish a ground truth reference forvalidating the efficacy of the metabolic state model according to someembodiments. Briefly, three healthy young men, ages 21-22, weights 62-72kg, heights 174-180 cm, and body fats 11-14%, with no family history ofdiabetes or obesity participated. During the first three days, thesubjects consumed a restricted diet, high in fat and low incarbohydrates, and followed an exercise program to deplete theirglycogen stores prior to tracking the energy balance over a period often days. Halfway through this energy restrictive period, the subjectswere admitted into a whole room indirect calorimetry chamber in whichrespiratory exchange measurements were to be continued for ten days.After 36 hours in the chamber, their diet was changed to ahigh-carbohydrate, low-fat diet that was ingested for the followingseven days. During the last two days, while still in the chamber, thesubjects received limited amounts of a high protein diet essentiallydevoid of carbohydrates. The subjects then left the respiration chamberbut continued to consume the high-fat, low-carbohydrate diet for afurther two days.

The restricted high-fat, low-carbohydrate diet consumed on days 1-3, 13,and 14 provided 1600 kcal comprising 15% protein, 75% fat, and 10%carbohydrate. During the overfeeding period (days 4-10 inclusive), thehigh-carbohydrate, low-fat diet provided 3600 kcal, comprising 11%protein, 3% fat, and 86% carbohydrate. Energy intake was increasedprogressively each day while the composition was kept constant with theintent to provide 1500 kcal in excess of the previous day's energyexpenditure, which was measured in the respiration chamber. By day 10the energy intake had thus increased to 5000 kcal. For the next (andlast) two days in the respiration chamber, the subjects consumed ahigh-protein, low-calorie diet (protein sparing modified fat, 600 kcal).The diet was then changed to the same restricted high-fat,low-carbohydrate diet eaten on days 1-3 for the last 2 days of theexperiment spent outside the chamber.

FIGS. 14A and 14B are plots illustrating the results in carbohydrateintake and daily RQ average of this carbohydrate overfeeding experiment.Note that from the onset of carbohydrate overfeeding starting on day 4,it took more than 24 hours for the daily average RQ to exceed 1.0 from aglycogen-depleted starting point of 0.75, and 4 days for thecarbohydrate over feeding to effectively saturate the glycogen storagecapacity and raise daily average RQ to its maximum observed value of1.16.

FIGS. 15A-15F are plots comparing published results from thecarbohydrate overfeeding experiment with results produced by a metabolicstate model simulation in accordance with some embodiments. Themetabolic model was initialized with the data corresponding to thedietary intake, activity, and average physiology of the three subjectsin the carbohydrate overfeeding experiment. The solid curves in FIGS.15A-15F represent the composite values for the three individualsinvolved in the study as reported in the published paper. With theexception of FIG. 15C, the dotted curves represent the values predictedby the metabolic state model given the feeding and exercise schedulesduring the study. In some embodiments, the model may include a proteinmodule; however, in this case, the caloric values of the proteinmacronutrients, shown in FIG. 15A, were lumped with the fat input.Unlike the study, which took fourteen days to complete, the simulationof the study completed in less than one second.

A number of the measured parameters from the study are compared to thesimulation output, including total calorie intake in FIG. 15A, dailyenergy expenditure in FIG. 15B, total energy expenditure in FIG. 15C,unhydrated glycogen storage in FIG. 15D, change in fat stores in FIG.15E, and RQ in FIG. 15F. The degree of correlation between the publisheddata and the simulated output provides compelling evidence that, withoutresorting to detailed modeling of cellular metabolic pathways andprocesses, the impact of dietary macronutrients and exercise can becaptured by this system-level feedback control model.

Realizing that blood glucose control is a critical function that takesprecedence over other functions of the homeostatic control system leadsto a somewhat different perspective regarding the causes and treatmentsfor excess body weight and obesity. TABLE 5 below compares some of theimplications of the metabolic state model to commonly held views.

TABLE 5 Metric Conventional View Model View Obesity Contributor toMechanism by which T2-diabetes the body attempts to avoid becomingdiabetic High insulin Caused by insensitivity Caused by overwhelminglevels to Insulin (Insulin the 4 glucose control Resistance) actuatorsDiet for Requires overt Requires active avoidance weight loss control ofcalorie of carbohydrate intake/expenditure “overconsumption” Exercisefor Low intensity is best Choose exercise to counteract weight lossbecause it's fat burning macro-nutrient imbalance Feedback for On-demandbody weight On-demand metabolic state weight loss measurements (scale)measurements (RQ, insulin, etc.) Excess body Root causes are Root causesare historical weight decreased activity changes in macronutrientpopulation and larger food mix that subvert homeostatic trend portionscontrol loops

As noted above, the human body has only four actions to take to controlblood glucose levels and two of these—shutting off fat burning andturning glucose into fat—lead to weight gain. Consequently, weight gainleading eventually to obesity is not necessarily the cause of diabetes,but rather the body's best effort to avoid becoming diabetic by clearingexcess glucose from the blood stream. Eventually, when these fourprocesses are overwhelmed, the diagnosis is diabetes, but it is notcaused by obesity.

Similarly, high insulin levels are often attributed to insulininsensitivity whereas the model clearly shows that the glucose controloptions are rate limited, and as the pancreas increases the controlsignal (insulin), eventually the rate limits of the processes arereached. Over time, the burden of persistently high blood glucose levelsmay degrade the effectiveness of the control mechanisms, but the modelsuggests that the degradation of the response to insulin (insulinresistance) is not the precipitating event but a consequence oflong-term overburdening of the glucose control mechanisms.

The fact that body weight may fluctuate by a kilogram or more during thecourse of a day and that body weight fluctuations are caused by a numberof processes not related to fat gain or loss, means that body weight isonly a reliable indicator of progress over time periods of many days orweeks. As a consequence, making the association between dietary andexercise choices and their impact on weight loss goals on any given day,whether good or bad, is challenging to discern by tracking body weight,since the impact may not be discernable for days.

Apparatus for Measuring RQ

Given the differences in the way individuals respond to dietarymacronutrients and exercise, the challenge is provide feedback, ondemand, regarding an individual's metabolic state. On a long-term basis,there are methods for quantifying metabolic health through blood drawsand analysis, and many individuals obtain snapshots of their metabolichealth during an annual physical. However, to control weight and assessdaily choices regarding macronutrient intake and exercise, it would bebeneficial to be able to measure blood glucose levels, insulin levels,and disposition of macronutrients on a daily or hourly basis. While anumber of blood analysis measurements can now be made by means of fingerpricks and reagent strips, including blood glucose and ketone levels,these measurements are invasive, require consumables which can be costlyover time and, most importantly, don't directly reveal insulin levels orenergy substrate mix.

However, referring to the block diagram of FIG. 3, there is a simple,non-invasive method of measuring individual energy substrate mix andinferring the state of three of the four metabolic control valves 326,328, 330, and 332 in the diagram. While not a direct measure of insulinlevels, knowledge of the state of these valves conveys information aboutmetabolic health and normalcy of insulin levels. According to someembodiments, this may be determined non-invasively from an analysis ofexhaled breath. The method is based on a simple breath analysis tomeasure a quantity known as the respiratory exchange ratio (RER) which,in the absence of short-term breathing and anaerobic exercise artifacts,is representative of the respiratory quotient (RQ) at the cellularlevel.

The burning of fat and carbohydrates to supply metabolic energy needs isessentially a combustion process and, as such, consumes oxygen (O₂) andproduces combustion by-products, in particular carbon dioxide (CO₂).Furthermore, the ratio of CO₂ produced to O₂ consumed is different forfat burning than for carbohydrate burning. At the cellular level, theratio of CO₂ produced to O₂ consumed is called the respiratory quotient(RQ) and is equal to 1.0 for carbohydrate combustion and 0.7 for fatcombustion. If carbohydrates are being used exclusively to supply all ofthe metabolic energy needs of the whole body, the RQ will be about 1.0and if fats are being used predominantly, the RQ will be closer to 0.7.As indicated in the discussion of the metabolic model, in general,metabolic energy needs are met by a combination of carbohydrate and fatburning, in which case the value of RQ provides an indication of thepercent of metabolic energy derived from carbs versus the percentderived from fats.

The O₂ needed for combusting carbs and fats is drawn from inhaled air,and similarly, the CO₂ produced by the processes is expelled in exhaledair. Consequently, under a fairly broad set of conditions, measuring thegas concentration of an individual's exhaled breath, to determine the O₂consumed and the CO₂ produced, provides all the information needed todetermine the percent of metabolic energy derived from carbs versusfats. Protein can also be used to supply metabolic energy, but it is aminor player in supplying metabolic energy needs and, in any case, hasan RQ in the mid-range between carbs and fats, nominally about 0.8depending upon the details of the protein.

Clearly the ability to measure and track one's RQ would provide valuableinformation about the impact of specific food and exercise choices onmetabolism, and thus provides a means to assess how dietarymacronutrient choices and levels of activity drive the body's choice offuels. The implication is that if a low-cost personal sensor can bedeveloped, it can be used to provide information to the user on-demandregarding the current state of his metabolic system. Armed with such asensor, one could, if desired, track the impact of dietary and exercisechoices on, for example, an hour-by-hour basis including during exerciseitself.

Apparatus for measuring and/or tracking RQ are disclosed according tosome embodiments. An RQ device may provide real-time assessment of asubject's metabolic state. Because of the real-time measurement capacityof a subject's RQ device, RQ measurements may be used to effectimmediate changes in a subject's behaviors that affect the subject'smetabolic state, such as, for example, modification of nutritionalintake and/or activity level. According to some embodiments, an RQdevice includes and/or is communicatively coupled with acomputer-implemented metabolic state model.

In some embodiments, such as those illustrated in FIGS. 16-19, systemsand apparatus for measuring RQ use low-cost commercial electro-opticalsensors which, because of their small size, weight, and power, have timeconstants longer than a breath. Consequently, the sensor designemphasizes the capture and holding of end-tidal portions of the exhaledbreath sample in the measurement chamber during the inhale cycle inorder to allow the slow sensors sufficient time to settle to an accuratemeasurement. The challenges to implementing this direct breath samplingsensor include:

-   -   1) avoiding condensation on the sensor, which is achieved in at        least some embodiments by heating the sensor to the expected        temperature of the exhaled breath;    -   2) preventing ambient air from entering the measurement chamber        during inhalation, which is achieved in at least some        embodiments by using multiple large valves to ensure low        respiratory burden;    -   3) correcting for changes in measurement chamber conditions,        which is achieved in at least some embodiments by providing        sufficiently fast and accurate temperature and humidity sensors;    -   4) correcting for the inability of at least some sensors (too        slow) to measure temporal variations in O₂ and CO₂        concentrations over each breath profile, which is achieved in at        least some embodiments by incorporating an adaptive energy        expenditure estimation algorithm.

FIG. 16 is a cross-sectional view of a portable RQ device 1600 inaccordance with some embodiments. In FIG. 16, the RQ device 1600includes a mouthpiece socket 1602, a flow rate aperture 1604, and anintake flow valve 1606 for receiving expired breath from a subject. Asshown in FIG. 16, an input port on the device (i.e., the mouthpiecesocket 1602) may be designed to be compatible with a sports cap on adisposable bottle, thereby providing for an inexpensive source ofmouthpieces for the same or multiple users. The RQ device in FIG. 16also defines a measurement chamber with sensors 1608 positioned therein.Printed circuit boards (PCBs) 1610, a display 1612, a purge fan 1614,and a battery 1616 are also included in accordance with someembodiments.

FIGS. 17A-17G are perspective views of components of the RQ device inFIG. 16 in accordance with some embodiments. FIGS. 17A-17C show moredetailed views of the body of the device. FIG. 17D shows the circuitboards, FIG. 17E shows a flow sensor, FIG. 17F shows an O₂ sensor, andFIG. 17G shows a CO₂ sensor.

FIG. 18A is a perspective view and FIG. 18B is a cross-sectional view ofa second RQ device in accordance with some embodiments. The RQ deviceFIG. 19 is another perspective view of the RQ device in FIG. 18A inaccordance with some embodiments.

An RQ device may have at least one input port. In some embodiments, asubject breathes in and out through a mouthpiece and/or cap that isattached to an input port of an RQ device. In other embodiments, asubject breathes in and out through one or more nasal tubes attached toan input port. The sampling of the subject's breath may be hands-free toallow for continuous sampling of exhaled breath without interfering withconversation, consumption of food and drink, or other activities and/orto facilitate the use of the device during movement or exercise. Forexample, a subject may be fitted with nasal tubes and/or a mouthpiecethat connects to an input port and, optionally, is secured around thesubject's head, thus allowing for hands-free sampling. Other hands-freeconfigurations may include securing sampling tubes around a subject'storso, arm, neck, or lower body. Hands-free sampling is enabled, atleast in part, because, unlike energy expenditure (Calorie)measurements, measurement of RQ does not require measuring of volumerates.

In some embodiments, an RQ device is fitted with one or more valves thatcontrol flow of air through the device during subject exhalation (e.g.,an exhaust valve) and/or inhalation (e.g., an intake flow valve). Avalve may help enforce unidirectional flow of air during the subject'sbreathing. Unidirectional airflow, in tum, may support more accuratemeasurements. A valve also may aid in capturing a subject's end tidalair, as explained further below. In some embodiments, at least one valveis provided for exhalation. Two, three, four, five, six, seven, eight,nine, ten, fifteen, or twenty valves may be provided for exhalation. Insome embodiments, at least one valve is provided for inhalation. Two,three, four, five, six, seven, eight, nine, ten, fifteen, or twentyvalves may be provided for inhalation. Different types of valves may beused. For example, the intake flow valve in FIG. 16 is a flapper valve.Flapper valves are also used as the intake and exhaust valves in FIGS.18A and 18B in accordance with some embodiments.

In some embodiments, an RQ device includes a fan to remove or at leastpartially remove gases from the device and/or prevent or at leastpartially reduce condensation in the device. A fan may also contributeto keeping the measurement chamber closed during the initial period ofexhalation and/or open only during the end-tidal portion of the breath.For example, the RQ device in FIG. 6 shows a purge fan. In someembodiments, an RQ device includes a shutter to enable the capture andmeasurement of a subject's expired end tidal air fraction, as explainedfurther below.

According to some embodiments, an RQ device includes at least oneelectronic component for controlling and/or communicating informationabout the device including, but not limited to, measurements made withthe device. The electronic components may be electrically coupled usingone or more printed circuit boards as shown in FIGS. 16 and 17D.Additional electronic components for controlling and/or communicatinginformation about the device may be external to the RQ device butcommunicatively coupled (with wires or wirelessly) with the RQ device asshown in FIG. 18A. An RQ device may be further configured to house aninternal battery, as shown in FIG. 16, and/or to be coupled with anexternal power source, as shown in FIG. 18A. An internal battery may berechargeable (with wires or wirelessly) or disposable. In furtherembodiments, an RQ device may include a user interface. The userinterface may include an input component and/or an output component(e.g., visual, aural, and/or haptic signals or displays). For example,the RQ device in FIG. 16 includes a visual display screen. Alternativelyor in addition, an RQ device may be coupled (with wires or wirelessly)with an external input device and/or output device (e.g., a smartphone,tablet, laptop, or other computing device).

In some embodiments, an RQ device includes one or more sensors ordetectors. The one or more sensors may include a flow sensor and/or agas sensor.

A flow sensor is a component for sensing a rate of fluid flow and mayinclude, but is not limited to, a microsensor that measures the transferof heat caused by the moving fluid (e.g., a thermal mass flow meter), alaser-based interferometer, a photoacoustic Doppler sensor, and/or amechanical flapper valve or vane that is pushed by the fluid to drive,for example, a rotary potentiometer.

An RQ device may sample and/or measure a subject's end tidal breath,maximum expired CO₂, and/or minimum expired O_(2.) According to someembodiments, an O₂ sensor and/or a CO₂ sensor is arranged to capture theend tidal concentrations of O₂ and/or CO₂, respectively.

A gas sensor is a component for sensing and/or measuring one or more gastypes and may include, but is not limited to, an electrochemical gassensor, an infrared point sensor, an infrared imaging sensor, asemiconductor, a spectrophotometer, a tunable diode laser spectrometer,photoacoustic spectrometer, and/or a holographic gas sensor. In someembodiments, a gas sensor is an all-optical gas sensor for detecting andmeasuring a gas concentration using the characteristic optical/spectralabsorption of the gas species. A gas sensor may be a slow gas analysissensor.

According to some embodiment, optical gas sensing technology reduces theneed for expendables and/or frequent replacement of consumable sensorelements such as electrochemical fuel cell sensors. For example,commercial sensors may use an electrochemical cell for O₂ detection, theanode of which is consumed during measurements such that the sensorrequires frequent re-calibration and eventual replacement (typicallyafter 6-12 months of use). Thus, an all-optical gas sensor may be usedto minimize size, weight, power, and/or use of consumables.

In some embodiments, an RQ device includes a CO₂ all-optical gas sensorand/or an O₂ all-optical gas sensor. For example, an O₂ sensor may be afluorescence quenching sensor, as shown in FIG. 17F, and/or a CO₂ sensormay be a nondispersive infrared (NDIR) sensor, as shown in FIG. 17G. Insome embodiments, the housing/packaging of a commercially-availablesensor (e.g., the O₂ sensor in FIG. 17F) may be modified by removing anapical portion of the housing and/or modulating a thickness of aruthenium component of the sensor.

One challenge of using these low-power, low-cost sensors for themeasurement of O₂ and/or CO₂ is that the sensor time constants may belong compared to the respiration periods and the time constants may beunequal in some embodiments. In order to compensate for the long and/orunequal time constants associated with the sensors, the measurementchamber of the device may be designed to be relatively small andrelatively close to the subject's mouth. Such embodiments account forthe effect of dead space air (i.e., the volume of air that is inhaledbut does not take part in gas exchange). The dead space air associatedwith the mouth, esophagus, and upper lungs is exhaled first andsubsequently pushed through the measurement chamber by remainingexhalant until, finally, the end tidal breath is captured in themeasurement chamber where it resides during the next inhalation cycle.

In some embodiments, as a consequence, these slow gas analysis sensorsare exposed to end tidal air during most of a breathing cycle andtherefore asymptotically approach the true end tidal concentration of O₂and CO₂ with only brief exposure to dead space air during the nextexhalation. This technique of trapping only end tidal air in themeasurement chamber may be accomplished through the use of low-cost,mechanical valves, as discussed above, that consume minimal or noadditional power in accordance with some embodiments. The influence of asubject's dead space air may be reduced further by the use of amechanical shutter, a vacuum pump, and/or a fan timed to keep themeasurement chamber closed during the initial period of exhalation, andopen only during the end tidal portion of the subject's breath.

Another challenge associated with the unequal time constants of the O₂and CO₂ sensors is that at any instant of time, one sensor may havesettled to, for example, about 90% of the final value while the slowersensor may only have settled to, for example, about 50% of the finalvalue. Therefore, taking a ratio of the CO₂ concentration to the O₂concentration measured at the same time may produce large errors. Inorder to contend with the unequal time constants of the sensors, in someembodiments, the measurements are not time-synchronized. Instead, amaximum value of CO₂ during a breath is measured, a minimum value of O₂during a breath is measured, and these measurement are used to estimatethe RQ value.

Following the acquisition of the minimum expired O₂ (i.e., end tidal O₂sample) and the maximum expired CO₂ (i.e., end tidal CO₂ sample) theaverage RQ is computed over valid breaths as follows:

$\begin{matrix}{{RQ_{AVG}} = \frac{\Sigma\left\lbrack {\frac{{CO}_{2exp}}{N_{2exp}} - \frac{{CO}_{2in}}{N_{2in}}} \right\rbrack}{\Sigma\left\lbrack {\frac{O_{2in}}{N_{2in}} - \frac{O_{2exp}}{N_{2exp}}} \right\rbrack}} & (6)\end{matrix}$

FIG. 20 provides a process flow chart illustrating a method for breathsampling 2000 in accordance with some embodiments. In step 2002, gassensors are sampled at at least 1 Hz. In step 2004, offset and gaincorrection is applied to raw sample data with two-point (gain andoffset) calibration constants for O₂ and CO₂ sensors 2006. In step 2008,the fraction of expired nitrogen is computed using the followingequation:

%N_(2exp)=1−%O_(2exp)%CO_(2exp)   (7)

In step 2010, the fraction of oxygen consumed is computed according to:

%O_(2in)=20.9%−%O_(2exp)   (8)

In step 2012, the mean and stand deviations are computed. Sampleoutliers are discarded. If the fraction of expired oxygen has reached aminimum 2014 and fraction of expired carbon dioxide has reached amaximum 2016, then an end tidal oxygen sample 2018 and an end tidalcarbon dioxide sample 2020 are used to compute average RQ over validbreaths in step 2022 according to equation (6) above.

Over a period of extended use, all gas sensing technologies are subjectto drift. As a result, existing indirect calorimetric sensors eitherrequire frequent re-calibration with a precision gas mixture, or the useof a pre-calibrated expendable cartridge. An important improvement insome embodiments of an RQ device is the ability for a user to quicklyand easily calibrate the sensor using an inexpensive and readilyavailable CO₂ cartridge. Calibration of an O₂ sensor and/or CO₂ sensormay be achieved by performing a zero (offset term) and a span (gainterm) calibration on each sensor. Typically this process would beperformed by a trained technician in a laboratory using calibrated gasmixtures; however, that is unnecessary according to at least someembodiments. In some embodiments the zero gas concentration conditionfor both sensors can be met by purging the sensor chamber with ahalocarbon gas obtained from a commercially-available aerosol “gasduster” container.

First, all-optical gas sensors may require less frequent full-scalecalibration. In accordance with some embodiments, an RQ device, whenturned on, may automatically or manually undergo a pre-use ambient aircalibration that takes into consideration average ambient air levels ofO₂ and CO₂. A limited ambient-air calibration may be performed each timea sensor is powered up. Ambient air calibration may assume that, exceptin very confined rooms with inadequate ventilation, dry ambient air O₂concentration is about 20.9%, which may be adjusted downward to accountfor relative humidity, and CO₂ concentration is about 0.04%. Ambient-aircalibration allows for an adjustment or correction of gain in an O₂sensor (most susceptible to gain drift) and/or an adjustment orcorrection of offset in a CO₂ sensor (most susceptible to offset drift).Thus, in some embodiments, ambient-air calibration helps to maintainsensor accuracy over time.

Historical ambient-air calibration measurements may be stored in and/oraccessible to an RQ device for subsequent comparison to future ambientO₂ and CO₂ measurements in order to determine an amount of drift in thesettings. If predetermined amount of drift in the settings is exceeded,a full calibration cycle may be performed by the user.

A full calibration may be implemented, when necessary, automatically ormanually by, for example, using a CO₂ cartridge in connection with theambient-air calibration procedure. First, the O₂ gain and/or CO₂ offsetmay be computed at power up from ambient air. Subsequently, CO₂cartridge may be introduced into the measurement chamber of an RQdevice. In some embodiments, an RQ device includes a port or chamber forreceiving CO₂ into the measurement chamber from a CO₂ cartridge. Byfilling the measurement chamber with CO₂, residual O₂ is purged or atleast reduced. At that point, the zero offset for O₂ may be determinedso that a full two-point calibration of the O₂ sensor is achieved. Asthe CO₂ leaks from the measurement chamber, it is replaced by ambientair, with a known or calculable concentration of O₂ (e.g., about 20.9%)and CO₂ (e.g., about 0.04%) in accordance with some embodiments. Becausethe O₂ sensor has undergone a two-point offset and span calibration, itmay serve as a calibrated reference, which together with the known orcalculable concentration of O₂, may be used to determine a spancalibration for the CO₂ sensor. For example, when the O₂ sensor readsabout 16.72%, then about 80% of the CO₂ has leaked out of themeasurement chamber and been replaced with ambient air, so the CO₂sensor should measure a concentration of about 20%.

Although these sensors offer long term stability, another challengeassociated with using optical gas sensors is that the optics are subjectto contamination resulting in a loss of calibration. Becausecontamination of optical surfaces on a sensor may degrade accuracy ofsubsequent measurements and the humidity of exhaled air is close to100%, a sensor may be heated to a temperature slightly higher than, forexample, the subject's body temperature, in order to exceed the dewpoint and thus prevent condensation of moisture from exhaled breath onthe sensor's optical surfaces in accordance with some embodiments. Insome embodiments, a sensor is automatically or manually heated orallowed to warm to a predefined temperature including, but not limitedto, 30° C., 31° C., 32° C., 33° C., 34° C., 35° C., 36° C., 37° C., 38°C., 39° C., 40° C., 45° C., or 50° C.

FIG. 21 is a process flow chart illustrating a method for RQ devicecalibration 2100 in accordance with some embodiments. A history ofambient-air span and zero recalibrations 2102 is maintained. In step2104, drift history is reviewed to determine whether full calibration isneeded. If not, step 2106 is performed to update the calibrationhistory. In step 2108, ambient oxygen and carbon dioxide levels arereviewed to determine whether they are within specification. If theyare, no calibration is required 2110. If they are not withinspecification, oxygen span or carbon dioxide zero-point is recalibratedin step 2112 and the calibration history is updated 2106. However, ifdrift history indicates that full calibration is needed, the sensormeasurement chamber is allowed to fill with ambient air in step 2114,oxygen span is set to 20.9% and carbon dioxide zero-point is set to0.04% in step 2116, a carbon dioxide cartridge is attached to themeasurement chamber valve to purge the chamber in step 2118, oxygen isset to 0% in step 2120, and calibration is continued as carbon dioxideleaks out over time 2122. Once the oxygen sensor reading is 16.7±0.1% instep 2124, the carbon dioxide span is set to 20% in step 2126, and fullcalibration is completed 2128.

The evolution of the design of the RQ devices in FIGS. 16, 17A-17G,18A-18B, and 19, including the challenges associated with certainaspects of RQ measurement and the solutions provided, are detailed belowin TABLE 6.

TABLE 6 1st Gen Challenge Approach Problems Innovation Breath samplingCapture single RQ variable due to Average over end-tidal breathventilation artifacts several breaths on a single breath Multiple breathRequire deep, slow Accentuates Physically modify averaging breaths toaccommodate ventilation artifacts COTS sensor slow O₂ sensor to gainabout 10x speed up Demarcate each Use CO₂ waveform O₂ min and CO₂Average O₂ and breath sample and shape to reliably max not necessarilyCO₂ separately rather average RQs demarcate breaths phased in time thanbreath-by-breath RQs Inhaling through Inhale through Abnormal breathingIncorporate flapper sensor dilutes the nose and pattern creates valvesto allow mouth sample volume exhale through ventilation artifactsbreathing exclusively the mouth Number of breath Observe real-time Not aviable Compute mean samples needed RQ plot until approach for a andvariance and steady state reached non-technical user stop when variancesufficiently small Ventilation artifacts Observe real-time Not a viableDiscard outliers based from cough, laugh, RQ plot to visually approachfor a on history of yawn, etc. detect artifacts non-technical userbreath-by-breath averages Condensation Employ metal The power and Heatonly the of about 100% RH case and heat to time to reach condensationsensitive breath fogs optics 38° C. to avoid 38° C. is prohibitivecomponents condensation Thermal insulation Insulate measurement Foamtends to Employ impermeable to speed and hold chamber with foampreferentially absorb insulation materials 38° C. temperature gascomponents CO₂ sensor Use known gas Not a viable approach Employ CO₂sensor calibration drifts mixtures to recalibrate for low-cost modelthat allows over time CO₂ sensor personal use calibration from ambientair O₂ sensor calibration Use known gas Not a viable Employ O₂ sensordrifts over time mixtures to recalibrate approach for low- model thatallows O₂ sensor cost personal use calibration from ambient air RQ canbe biased Make only Unreliable RQ Incorporate flow by lactate bufferingresting RQ measurements during rate sensor to gauge measurements intenseexercise exercise intensity from VO₂

According to some embodiments, an RQ device is designed and/or used tomeasure resting RQ. The underlying rational for measuring resting RQ isthat the energy substrate mix at rest provides for a more accurateassessment of a subject's metabolic state. One design challenge toacquiring the subject's resting RQ is a potential for variations in thesubject's breathing rate and ventilation, which would bias the RQmeasurement. Thus, in some embodiments, an RQ device is designed tocompute an RQ measurement based on an average of several breaths.

An RQ device may measure CO₂ concentration at a high temporal rate(e.g., at least 5 times the breathing rate) to track the CO₂ variationover time. The CO₂ concentration measurements may be used to demarcateeach breath and thereby ascertain a resting respiration rate. Thevariability in respiration rate, together with the measurement historyof the O₂ and CO₂ concentrations, may be used to determine when asubject's breathing is stable, thereby producing a reliablerepresentation of the subject's resting RQ. In some embodiments, ahistory of a subject's resting respiration rate and/or a measurement ofthe subject's heart rate are used to distinguish resting RQ measurementsfrom RQ measurements made during periods of activity or exercise.

FIG. 22 is a process flow chart illustrating a method of RQ measurement2200 in accordance with some embodiments. A daily baseline of restingRQs (e.g., upon waking, before/after meals, before/after exercise,and/or before bedtime) 2202 is maintained. In step 2204, RQ measurementis initiated. In step 2206, the RQ sensor is turned on. Once a readyindication is received in 2208, indicating that the gas sensors havewarmed to 38° C. and/or ambient-air auto-calibration has executed,ambient air calibration correction history may be updated in step 2210.In step 2212, a user breathes normally into the sensor through amouthpiece and/or nasal cannula. The sensor automatically detects newbreath and determines maximum carbon dioxide in each breath and minimumoxygen in each breath in step 2214. Average carbon dioxide samples andstandard deviations are computed as are average oxygen samples andstandard deviations in step 2216. In step 2218, the standard deviationsare compared to stability limits. If they fall outside, outliers arediscarded in step 2220 and the process returns to step 2214. However, ifthey fall within, RQ is computed in step 2222, optionally archived instep 2224 (e.g., for further optimization), and RQ is displayed via anaudio, graphic, or tactile indication 2226.

According to some embodiments, a subject may take RQ readings at varioustimes throughout a day as defined by actual time and/or activity. Forexample, a subject may measure RQ before and/or after fasting, consumingfood and/or drink, exercising, and/or sleeping. In some embodiments, asubject may measure RQ at specific time intervals including, but notlimited to, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7hours, 8 hours, 9 hours, 10 hours, 11 hours, or 12 hours before or afteran activity.

In some embodiments, a subject breathes into an RQ device, througheither a mouthpiece or a nasal cannula (e.g., in a hands-freeconfiguration). At least one sensor detects new breaths, a maximum CO₂concentration per breath, and/or a minimum O₂ concentration per breath.In some embodiments, an RQ device computes the average CO₂ concentrationand the average O₂ concentration to determine a standard deviation (SD)of the readings. If the SD of the readings is within predefined limits,an RQ measurement may be output including, but not limited to,displayed, stored, and/or transmitted. If the SD of the readings isoutside the predefined limits, the outlier readings may be discarded. Inthat case, the subject may continue to breathe into the RQ device untilreadings that fall within the acceptable SD range are collected.

In some embodiments, RQ measurements provide a way to finely monitor andtune a subject's metabolic state as a source of individual biofeedback.RQ values may impart: instant knowledge of the fraction of fat versuscarbohydrate energy a subject is utilizing; an indication of whether asubject is in a de novo lipogenesis metabolic state (i.e., convertingexcess blood glucose into fat); (3) an indication of carbohydrate stress(implying high blood glucose levels); and/or (4) an understanding ofglycogen capacity. These personal biofeedback indicators make itpossible to track a subject's metabolic state and/or modify diet andexercise accordingly to, for example, achieve/maintain a weight goal,manage metabolic disease, and/or improve metabolic fitness andendurance.

Unlike calorie counting, in some embodiments, RQ measurements encouragediet and exercise adjustment by providing on-demand feedback regardingthe impact of macronutrient choices and activity levels to avoidcarbohydrate overloading that in tum leads to storage, rather thanmetabolic combustion, of dietary fats as well as conversion of glucoseto stored fat via de novo lipogenesis, both of which contributeeventually to obesity. In some embodiments the personalized biofeedbackcapacity of the RQ measurement device is used to manage or reduce thelikelihood of type 2 diabetes. RQ measurements may replace or reduce thefrequency of capillary blood glucose measurements required to manageblood sugar. For example, resting RQ of 2:1.0 indicates high bloodglucose (hyperglycemia), whereas resting RQ of about 0.7 indicates arange of normal to low blood glucose (hypoglycemia).

The use of routine RQ measurements also may support individualdiagnostics. In some embodiments, a database of “normal” or historicallevels of RQ measurements under various conditions (e.g., fasting,glucose challenge, post-prandial, etc.) is maintained. When newlyacquired RQ readings consistently vary from the normal or historicalmeasurements, an indication of a loss of metabolic homeostasis may beprovided to the subject or a clinician for early warning as to the onsetof a disease state.

In some embodiments, RQ measurements may be used to monitor metabolicresponse. FIG. 23 is a graph illustrating RQ measurements from a subjectover three days in accordance with some embodiments. The subject is ahealthy adult male human who had been training for a half-marathon atthe time the RQ readings were collected. In FIG. 23, on October 11, twodays prior to running the half-marathon, the subject's RQ values weremeasured at six separate times. First, the subject's fasting RQ wasmeasured in the morning (10:09 AM), indicating that about 56% of theenergy utilized by the subject was obtained from carbohydrates. Aftereating a low-carbohydrate breakfast (i.e., an omelet) (11:36 AM), stillonly 56% of the energy utilized by the subject was obtained fromcarbohydrates. Note that following a period of activity (i.e., thirtyminutes of walking and swimming for half a mile) (1:15 PM), thecarbohydrate-based energy utilized by the subject fell from about 56% toabout 50%, indicating that the exercise affected the subject'smetabolism, increasing the proportion of energy supplied from fat versuscarbohydrates. At lunch the subject consumed a high carbohydrate meal(i.e., two pieces of pizza and half a cookie) (2:25 PM), after which thecarbohydrate-based energy utilized by the subject began to rise, firstto about 55% and, several hours later (4:40 PM), to about 95%. However,after further activity (i.e., a brisk walk) (5:11 PM), thecarbohydrate-based energy utilized by the subject dropped to about 11%,indicating that the subject's metabolism was dominated by thelow-intensity fat-burning exercise.

On October 12, the day before the half-marathon, the subject consumedlarge quantities of carbohydrates (i.e., “carb loading”). An RQ readingtaken later that evening (9:08 PM) indicated an RQ greater than 1.0implying that all of the energy utilized by the subject was obtainedfrom carbohydrates. Furthermore, the subject was in a de novolipogenesis metabolic state in which excessive blood glucose levels werebeing converted to stored fat. However, on October 13, three hours aftercompleting the half-marathon and before eating, the carbohydrate-basedenergy utilized by the subject fell to 11%, indicating that the subjectwas again in a high fat burning state due to the combination of no foodintake and depleted glycogen stores.

In some embodiments, RQ measurements may be used to predict or provide aguideline or suggestion for performing an activity. FIG. 24 is a graphillustrating representative metabolic fuel utilization as it relates tospeed/intensity in accordance with some embodiments. In particular, FIG.24 illustrates the utility of measuring RQ for the assessment of optimalrunning speed in a hypothetical marathon scenario. To first order, arunner bums about the same calories per unit distance regardless ofspeed (i.e., at high speed the distance is covered in less time butrequires a higher level of exertion than at lower speeds). In theabsence of nutritional supplements, running at maximum speed may depletethe runner's circulating blood glucose and glycogen stores, a phenomenonknown as “bonking” or “hitting the wall”, after which speed andperformance drop precipitously. As implied in FIG. 24 and illustrated byTABLE 7, knowledge of RQ versus running speed, enables a runner toselect a pace to achieve an appropriate mix of fuels (e.g., fats pluscarbohydrates versus carbohydrates alone) in order to delay or avoidglycogen depletion (i.e., running smart) and thus achieve better overallperformance than simply running at maximum intensity (i.e., runningfast).

TABLE 7 Parameter Run Fast Run Smart Calories expended (per mile) 130130 Initial glycogen reserves (Calories) 1500 1500 Initial speed (mph)8.5 7.2 Initial pace (min:sec per mile) 7:04 8:17 Distance to glycogendepletion (miles) 11.5 26.2 Peak RQ during 26.2 mi race 1.00 0.83Average RQ over 26.2 mi race 0.80 0.83 Average speed over 26.2 mi race(mph) 6.1 7.2 Average pace over 26.2 mi race (min:sec per mile) 9:508:17 Finish time (hr:min) 4:16 3:37

In some embodiments, RQ measurements may provide a predictive measure ofa subject's endurance. FIG. 25 is a graph illustrating energyutilization from carbohydrates in two subjects moving on a treadmill atincrementally increasing speeds in accordance with some embodiments.Both subjects are healthy adult male humans with resting energyexpenditures from carbohydrates of about 41% and about 52%,respectively. RQ values were measured at each speed, approximately everytwo minutes. The RQ readings indicate that a subject with the lowerresting RQ (i.e., a lower percentage energy usage from carbohydrates) isable to maintain a higher treadmill speed prior to reaching an allcarbohydrate energy consumption state. This anecdotal example alsounderscores the fact that individual metabolisms are unique and thusbenefit from personalized, on-demand information about metabolic state,rather than prescriptive, formulaic diets and exercise regimens.

FIG. 26A is an image illustrating another RQ sensor prototype inaccordance with some embodiments. The sensor employs optical techniquesto determine the concentration of oxygen and carbon dioxide in expiredbreath and consequently does not require expendables. The sensormeasures the oxygen consumed and carbon dioxide produced by averagingseveral breaths to account for short-term ventilation effects. FIG. 26Bis a series of representative plots illustrating the breath-by-breathdata and RQ obtained with the sensor shown in FIG. 26A in accordancewith some embodiments. For purposes of illustration, the elapsed timefor the measurements shown in FIG. 26B is a little over three minutes,but a shorter measurement, on the order of 30-60 s is adequate toprovide a stable estimate of RQ. Note from the plots in FIG. 26B thatwhile there is some variation in RQ the average is 0.93, indicating thata majority (77%) of the metabolic energy during the measurement is beingderived from carbohydrate burning.

A key application envisioned for such as sensor is to provide daily oreven hourly feedback to the user regarding dominant source of metabolicenergy, essentially their metabolic energy zone. In terms of weight lossby reduction of adipose tissue, an RQ that hovers around 1.0 throughoutthe day implies that there is little energy being derived from fat andwhat energy is derived from fat is probably coming from circulatingdietary fat rather than the stored fat that is the objective of theweight loss effort. Over-consumption of carbs, where overconsumption isdependent on the individual metabolism and energy needs, may even leadto an RQ>1, which is indicative of carbs being converted and stored asfat for future energy needs. Thus a necessary, but not sufficient,condition for weight loss through reduction of stored fat is for the RQto be below 1. The lower the RQ, the greater percentage of energyderived from fat and, if the individual is not overconsuming dietaryfat, the energy will be derived from stored fat producing weight loss.

By design, the metabolic model is intended to capture salient metabolicperformance with the least amount of complexity. Consequently, there aremany options for augmenting the model with additional modules (thusraising complexity) in order to simulate metabolic performance withgreater detail or over a wider range of dietary inputs and activitylevels.

A personal RQ sensor, coupled with a metabolic modeled tailored to theindividual, provides a basis for developing more effective regimens forweight loss, diabetes management, and athletic training programs. Ratherthan a one-size fits all formulaic diet for weight loss, the metabolicmodel and sensor provide the quantitative tools to tailor macronutrientintake and exercise activity to achieve weight loss and weightmanagement goals. A non-invasive personal RQ sensor has the potential todetect and track episodes of high blood glucose reducing the frequencyof capillary blood testing and providing a higher density of data tocorrelate against dietary intake and activity in order to develop anindividualized predictive model.

Passive Side-Stream Sampling

Existing breath-by-breath metabolic sensors employ relatively fast andexpensive gas sensors (e.g., with sample rate time constants on theorder of 150 ms or less to track variations in O₂ and CO₂ gasconcentrations over the course of a breath) and/or active pumps tosample and deliver exhalant to the measurement chamber at a constantvolume rate.

According to some embodiments, a passive side-stream sampling system mayachieve accurate RQ and energy expenditure estimates using slower, lessexpensive, smaller, lighter, and/or lower power gas sensors and alow-cost and/or low-power differential pressure sensor.

FIG. 27 is an image illustrating components of a passive side-streamsampling system according to some embodiments. In FIG. 27, a sample tube2700 includes a mouthpiece 2702 and a flow restrictor or venture 2704.Two pressure tubes 2706 connected to a differential pressure flow sensorare used to measure the volumetric flow of inhaled and exhaled breath.Venture 2704 is configured to create a back pressure which forces asmall fraction (e.g., a few percent) of the exhaled air into at leastone side-stream pick-off 2708 into a tube 2710 connected to a mixingchamber 2712 where the O₂ and CO₂ gas concentrations are measured. Insome embodiments, the flow restrictor 2704 provides a linearpressure/flow relationship, which then provides a stable sidestream gassplitting fraction over the entire breath flow range. In otherembodiments, the flow restrictor 2704 provides a more quadraticpressure/flow relationship, so the sidestream fraction increases athigher breath flow rates as compared to lower flow rates. In someembodiments, tube 2710 comprises a water vapor- andtemperature-permeable polymer membrane, such as Nafion® tubing,configured to reduce and/or equilibrate both the temperature andhumidity of the exhaled breath sample with ambient conditions. The rateof flow of the breath sample into mixing chamber 2712 may be setproportional to the flow rate of the exhaled air, thereby implementingproportional analog sampling of each breath without the need for anactive pump. In some embodiments, several breaths may be required todisplace all of the air in mixing chamber 2712, so the chamber acts as abreath gas integrator in which the O₂ and CO₂ gas concentrations areaveraged via mixing over several breaths and the rate of change iscommensurate with the time constants of slower gas sensors. A keyelement of the mixing chamber design is a low-cracking pressure (e.g.,<about 1 mm H₂O) exit or exhaust valve 2714 to sustain breath samplingeven at low pressures developed by the flow restrictor 2704 encounteredtoward the end of an exhale when the breath flow is low. The advantageof a sidestream flow consisting of a fixed-fraction of the mainstreambreath flow is that it allows gas averaging over the entire breath flowcomposition range, spanning from anatomical dead space gas throughend-tidal alveolar gas. This provides the same functionality of aDouglas bag sampling system, but in a much more compact and portableform factor. In some embodiments, a Universal Serial Bus (USB) cable2716 or another connector, cable, and/or protocol may be used forconnection, communication, and/or power supply between mixing chamber2712 and, for example, a processor, memory, and/or energy storagedevice.

FIG. 28A is an image and FIG. 28B is a perspective wireframe view of abreath sensing system which includes a mixing chamber in accordance withsome embodiments. In FIGS. 28A and 28B, the mixing chamber includes anexhaust valve 2802 and replaceable battery 2804, which could also be arechargeable battery. In some embodiments, a breath sensing system maybe configured for connection to a battery, a wired or wirelessconnection to a battery carried elsewhere on a user's body or locatednearby, or a connection to an external power supply (e.g., an AC plug).In some embodiments, this electrical connection includes a USB,FireWire®, and/or similar connectorized cable.

FIG. 29A is a representative plot illustrating O2 and CO2 gasconcentration curves produced by a passive side-stream sampling sensorin accordance with some embodiments. FIG. 29B is a representative plotillustrating respiratory quotient, and FIG. 29C is a plot of thecorresponding breath volume rate and energy expenditure estimatesassociated with the data from the passive side-stream sampling sensor inaccordance with some embodiments.

According to some embodiments, the benefits of employing passiveside-stream sampling with a miniature mixing chamber overbreath-by-breath approaches include, but are not limited to, thefollowing:

-   -   utilization of smaller, lighter, less expensive, and/or lower        power gas sensors for greater system efficiency and/or mobility;    -   passive proportional gas concentration sampling of each breath        instead of an active pump for greater system efficiency,        mobility, and/or simplicity (especially with respect to        calibration);    -   utilization of simple, low cost differential pressure flow        sensor and single exhaust valve for greater system efficiency        and/or simplicity;    -   reduced valve leakage due to low pressure of the side-stream        sampling during inhalation for greater system efficiency and/or        accuracy;    -   improved accounting for dead space contributions to expired        breath via breath averaging in the mixing chamber for greater        system efficiency and/or accuracy; and    -   more reliable energy estimates based on matching the temporal        dynamic of the gas concentrations to the time constants of the        sensors for greater system efficiency and/or accuracy.

Conclusion

While various inventive embodiments have been described and illustratedherein, those of ordinary skill in the art will readily envision avariety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the inventive embodiments describedherein. More generally, those skilled in the art will readily appreciatethat all parameters, dimensions, materials, and configurations describedherein are meant to be exemplary and that the actual parameters,dimensions, materials, and/or configurations will depend upon thespecific application or applications for which the inventive teachingsis/are used. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific inventive embodiments described herein. It is,therefore, to be understood that the foregoing embodiments are presentedby way of example only and that, within the scope of the appended claimsand equivalents thereto, inventive embodiments may be practicedotherwise than as specifically described and claimed. Inventiveembodiments of the present disclosure are directed to each individualfeature, system, article, material, kit, and/or method described herein.In addition, any combination of two or more such features, systems,articles, materials, kits, and/or methods, if such features, systems,articles, materials, kits, and/or methods are not mutually inconsistent,is included within the inventive scope of the present disclosure.

The above-described embodiments can be implemented in any of numerousways. For example, embodiments of modeling, monitoring, and managingmetabolism disclosed herein may be implemented using hardware, softwareor a combination thereof. When implemented in software, the softwarecode can be executed on any suitable processor or collection ofprocessors, whether provided in a single computer or distributed amongmultiple computers.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a PersonalDigital Assistant (PDA), a smart phone or any other suitable portable orfixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output.

Examples of input devices that can be used for a user interface includekeyboards, and pointing devices, such as mice, touch pads, anddigitizing tablets. As another example, a computer may receive inputinformation through speech recognition or in other audible format.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks, wired networks or fiber optic networks.

The various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools, and also may becompiled as executable machine language code or intermediate code thatis executed on a framework or virtual machine.

Also, various inventive concepts may be embodied as one or more methods,of which an example has been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety,including, but not limited to, the following:

1. Acheson et al., Am. J. Clin. Nutrition, 48:240-47 (1988);

2. Bouchard et al., Obesity Res., 2 (5):400-10 (1994);

3. Cruickshank et al., J. Physiol., 80 (2):179-92 (1933);

4. Ganz et al., Diabetology & Metabolic Syndrome, 6 (50): 1-8 (2014);

5. Guh et al., BMC Public Health, 9 (88):1-20 (2009);

6. Hargrove, J. Nutrition, 136 (12):2957-61 (2006);

7. Jebb et al., Am. J. Clin. Nutrition, 58:455-62 (1993);

8. Ladenheim, Drug Design, Dev. & Therapy, 1867-75 (2015);

9. Ludwig et al., JAMA, 311 (21):2167-68 (2014);

10. McDevitt et al., Am. J. Clin. Nutrition, 74 (6):737-46 (2001);

11. Mozaffarian et al., NEJM, 364 (25):2392-2404 (2011); and

12. Weiss et al., N. Eng. J. Med., 350 (23):2362-74 (2004).

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of’ or “exactly one of,” or, when usedin the claims, “consisting of” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (i.e., “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively, as set forth in the United States Patent Office Manual ofPatent Examining Procedures, Section 2111.03.

As used herein in the specification and in the claims, the terms“subject,” “individual,” “recipient,” “host,” “user,” and the like areused interchangeably to refer to a either a human or an animal subject.

As used herein in the specification and in the claims, the term“metabolism” refers to biochemical processes whereby nutrients areconverted to energy for use by a mammal. Metabolism can be furtherdefined as including “catabolic” and “anabolic” processes. A catabolicprocess refers to biochemical events that occur in a mammal that resultin the breaking apart of molecules. An anabolic process refers tobiochemical events that occur in a mammal that result in the creation ofmolecules.

As used herein in the specification and in the claims, the term“metabolic disease” may include, but is not limited to, type 1 diabetes,type 2 diabetes, obesity, gout, lipid disorders, hyperthyroidism,hypothyroidism, dyslipidemia, hypolipidemia, galactosemia,phenylketonuria, metabolic syndrome, or phenylketonuria. Diabetes is ametabolic disease that results in excess sugar in the blood and urine.Type 1 diabetes is characterized by a destruction of the pancreatic betacells that produce insulin. Type 2 diabetes is characterized by insulinresistance, wherein the insulin produced by the pancreatic beta cells isnot efficiently utilized to convert glucose to other energy forms.

As used herein in the specification and in the claims, the term “sensor”refers to a transducer that detects an aspect of the environment andprovides a corresponding output. The phrases “all-optical gas sensor” or“all-optical gas sensing” refer to a sensor(s) that utilizes optics inthe detection of specific wavelengths (e.g., IR spectroscopy) for thedetection of the presence of certain gases. One example of an alloptical gas sensor is the CO₂ nondispersive infrared (NDIR) optical gassensor.

As used herein in the specification and in the claims, the term“hands-free” refers to an ability of a user to operate or practice asystem, apparatus, or method without, or with only minimal use, of theuser's hand(s).

1-11. (canceled)
 12. A computer-facilitated method for modelingmetabolism in a subject and/or managing body weight of the subject, themethod comprising: determining from data related to the subject obtainedvia at least one input device: a macronutrient composition and caloricvalue of food consumed by the subject; an intensity and duration ofactivity by the subject; a rate and maximum capacity of glycogen storagein the subject; and a rate and maximum capacity of de novo lipogenesisin the subject; and optimizing, via at least one processor, a nonlinearfeedback model to model energy substrate utilization in the subjectbased on the macronutrient composition and caloric value of foodconsumed by the subject, the intensity and duration of activity by thesubject, the rate and maximum capacity of glycogen storage in thesubject, and the rate and maximum capacity of de novo lipogenesis in thesubject.
 13. The computer-facilitated method of claim 12, furthercomprising: obtaining metabolic data for the energy substrateutilization in the subject, the metabolic data including respiratoryquotient (RQ) data acquired from the subject.
 14. Thecomputer-facilitated method of claim 13, further comprising:controlling, via the at least one processor, operation of the optimizednonlinear feedback model based on the metabolic data to determine atarget value of one or more energy substrate utilization variables thatat least one of maintains and increases energy substrate utilization inthe subject.
 15. The computer-facilitated method of claim 14, whereinthe one or more energy substrate utilization variables comprise at leastone of: the macronutrient composition and caloric value of food consumedby the subject; and the intensity and duration of activity by thesubject.
 16. The computer-facilitated method of claim 12, furthercomprising: determining from data related to the subject obtained viathe at least one input device a quality and duration of sleep by thesubject, and wherein optimizing, via the at least one processor, thenonlinear feedback model is based further on the quality and duration ofsleep by the subject.
 17. The computer-facilitated method of claim 12,further comprising: obtaining at least one initial physiologicalparameter associated with the subject, the at least one initialphysiological parameter including an initial body weight of the subject;and


18. The computer-facilitated method of claim 17, further comprising:controlling, via the at least one processor, operation of the optimizednonlinear feedback model based on the at least one initial physiologicalparameter to determine a target value of one or more energy substrateutilization variables that at least one of maintains and alters the bodyweight of the subject
 19. The computer-facilitated method of claim 18,wherein the one or more energy substrate utilization variables compriseat least one of: the macronutrient composition and caloric value of foodconsumed by the subject; and the intensity and duration of activity bythe subject.
 20. The computer-facilitated method of claim 17, whereinthe at least one initial physiological parameter further comprises atleast one of height, age, gender, body mass index (BMI), body fatpercentage, waist circumference, hip circumference, and chestcircumference.
 21. A system for optimizing a nonlinear feedback model ofenergy substrate utilization in a subject, the system comprising: atleast one input device for obtaining data related to the subject; atleast one memory device for storing the data related to the subject andprocessor-executable instructions; and at least one processor incommunication with the at least one input device and the at least onememory device, wherein upon execution of the processor-executableinstructions, the at least one processor: determines from the datarelated to the subject: a macronutrient composition and caloric value offood consumed by the subject; an intensity and duration of activity bythe subject; a rate and maximum capacity of glycogen storage in thesubject; and a rate and maximum capacity of de novo lipogenesis in thesubject; and optimizes the nonlinear feedback model to model energysubstrate utilization in the subject based on the macronutrientcomposition and caloric value of food consumed by the subject, theintensity and duration of activity by the subject, the rate and maximumcapacity of glycogen storage in the subject, and the rate and maximumcapacity of de novo lipogenesis in the subject.
 22. The system of claim21, wherein the at least one processor, upon execution of theprocessor-executable instructions, optimizes the nonlinear feedbackmodel to model energy substrate utilization in the subject based on aquality and duration of sleep by the subject, and the one or more energysubstrate utilization variables comprise at least one of themacronutrient composition and caloric value of food consumed by thesubject or the intensity and duration of activity by the subject.
 23. Asystem for managing body weight of a subject, the system comprising: atleast one input device for obtaining data related to the subject; atleast one memory device for storing the data related to the subject andprocessor-executable instructions; and at least one processor incommunication with the at least one input device and the at least onememory device, wherein upon execution of the processor-executableinstructions, the at least one processor: determines from the datarelated to the subject at least one initial physiological parameterassociated with the subject, the at least one initial physiologicalparameter including an initial body weight of the subject; and controlsoperation of a nonlinear feedback model to determine, based on the atleast one initial physiological parameter, a target value of one or moreenergy substrate utilization variables that at least one of maintainsand alters the body weight of the subject, wherein: the nonlinearfeedback model is optimized to model energy substrate utilization in thesubject based on at least one of: a macronutrient composition andcaloric value of food consumed by the subject; an intensity and durationof activity by the subject; a rate and maximum capacity of glycogenstorage in the subject; a rate and maximum capacity of de novolipogenesis in the subject; and a quality and duration of sleep by thesubject; and the one or more energy substrate utilization variablescomprise at least one of: the macronutrient composition and caloricvalue of food consumed by the subject; and the intensity and duration ofactivity by the subject.
 24. The system of claim 23, wherein the atleast one initial physiological parameter further comprises at least oneof height, age, gender, body mass index (BMI), body fat percentage,waist circumference, hip circumference, and chest circumference.
 25. Thesystem of claim 23, wherein the nonlinear feedback model is optimizedfurther based on a quality and duration of sleep by the subject.